System and method for integrating user feedback into website building system services

ABSTRACT

A website building system (WBS) includes a processor implementing a machine learning feedback-based proposal module and a database storing at least the websites of a plurality of users of the WBS, and components of the websites. The module includes a plurality of per activity AI units and a feedback system. Each per activity AI unit supports one or more specific activity related to the WBS and provides at least one system suggestion to the users related to its specific activity. Each per activity AI unit includes at least one machine learning model suitable for the activity supported by its per activity AI unit. The feedback system provides a plurality of different kinds of feedback from the users for updating the machine learning models. The feedback system analyzes the feedback to determine which one of the at least one machine learning models to update.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. provisional patentapplications 62/853,191, filed May 28, 2019, 62/905,450, filed Sep. 25,2019, 62/970,034, filed Feb. 4, 2020, and 63/027,369, filed May 20,2020, all of which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to website building systems generally andto site creation based on feedback in particular.

BACKGROUND OF THE INVENTION

Website building systems are used by both novices and professionals tocreate interactive websites. Existing website building systems are basedon a visual editing model and most website building systems typicallyprovide multiple templates, with a template possibly including acomplete sample website, a website section, a single page or a sectionof a page.

Website building system users (also known as designers, subscribers,subscribing users or site editors) may design the website and thewebsite's end-users (the “users of users”) may access the websitescreated by the users. Although end-users typically access the system inread-only mode, website building systems (and websites) may allowend-users to perform changes to the web site, such as adding or editingdata records, adding talkbacks to news articles, adding blog entries toblogs, etc. The website building system may allow multiple levels ofusers (i.e. more than two levels) and may assign different permissionsand capabilities to each level. Users of the website building system mayregister in the website building system server which manages the users,their web sites and accesses by the end-users.

A website building system may be a standalone system or may be embeddedinside a larger editing system. It may also be on-line (i.e.applications are edited and stored on a server), off-line or partiallyon-line (with web sites being edited locally but uploaded to a centralserver for publishing). The website building system may use an internaldata architecture to store website building system-based sites and thisarchitecture may organize the handled sites' internal data and elementsinside the system. This architecture may be different from the externalview of the site (as seen, for example, by the end-users). It is alsotypically different from the way that the HTML pages sent to the browserare organized.

For example, the internal data architecture may contain additionalproperties for each element in the page (creator, creation time, accesspermissions, link to templates, SEO (search engine optimization) relatedinformation, etc.) which are relevant for the editing and maintenance ofthe site in the website building system, but are not externally visibleto end-users (or even to some editing users). The website buildingsystem may implement some of its functionality (including both editingand run-time functionality) on a server or server set, and some of itsfunctionality on client elements. The website building system may alsodetermine dynamically whether to perform some functionality on theserver or on the client platform.

A website building system typically handles the creation and editing ofvisually designed applications (such as a website). In some websitebuilding systems, such as those provided by Wix.Com, the applicationsconsist of pages, containers and components. Pages may be separatelydisplayed and contain components. Components may include containers aswell as atomic components.

The website building system may support hierarchical arrangements ofcomponents using atomic components (text, image, shape, video etc.) aswell as various types of container components which contain othercomponents (e.g. regular containers, single-page containers, multi-pagecontainers, gallery containers etc.). The sub-pages contained inside acontainer component are referred to as mini-pages, and each of which maycontain multiple components. Some container components may display justone of the mini-pages at a time, while others may display multiplemini-pages simultaneously.

The components may be content-less or may have internal content. Anexample of the first category is a star-shape component, which does nothave any internal content (though it has color, size, position,attributes and other parameters). An example of the second category is atext paragraph component, whose internal content includes the internaltext as well as the font, formatting and layout information (which isalso part of the content rather than being attributes of the component).This content may, of course, vary from one instance of the textparagraph component to another. Components which have content are oftenreferred to as fields (e.g. a “text field”).

Pages may use templates, general page templates or component templates.Specific cases for templates include the use of an application masterpage containing components replicated in all other regular pages, andthe use of an application header or footer (which repeat on all pages).Templates may be used for the complete page or for page sections. Thewebsite building system may provide inheritance between templates, pagesor components, possibly including multi-level inheritance, multipleinheritance and diamond inheritance (i.e. A inherits from B and C andboth B and C inherit from D).

The visual arrangement of components inside a page is called a layout.The website building system may also support dynamic layout processing,a process whereby the editing of a given component (or other changesaffecting it such as externally-driven content change) may affect othercomponents, as further described in U.S. Pat. No. 10,185,703 entitled“Website Design System Integrating Dynamic Layout and Dynamic Content”,granted on 22 Jan. 2019, commonly owned by the Applicant andincorporated herein by reference.

A website building system may be extended using add-on applications suchas a third party application and its components, list applications (suchas discussed in US Patent Publication No. US 2014/0282218 entitled“Website Building System Integrating Data Lists with DynamicCustomization and Adaptation” published 18 Sep. 2014, commonly owned bythe Applicant and incorporated herein by reference) and website buildingsystem configurable applications (such as described in in U.S. patentapplication Ser. No. 16/683,338 entitled “System And Method for Creationand Handling of Configurable Applications for Website Building Systems”,filed 14 Nov. 2019, commonly owned by the Applicant and incorporatedherein by reference). These third-party applications and listapplications may be added and integrated into designed websites.

Such third party applications and list applications may be purchased (orotherwise acquired) through a number of distribution mechanisms, such asbeing pre-included in the web site building system design environment,from an Application Store (integrated into the website building system'smarket store or external to it) or directly from the third-partyapplication vendor.

The third-party application may be hosted on the website building systemvendor's own servers, the third-party application vendor's server or ona fourth party server infrastructure.

The website building system may also allow procedural code to be addedto some or all of the system's entities. Such code could be written in astandard language (such as JavaScript), an extended version of astandard language or a language proprietary to the specific websitebuilding system. The executed code may reference API's provided by thewebsite building system itself or external providers. The code may alsoreference internal constructs and objects of the website buildingsystem, such as pages, components and their attributes.

The procedural code elements may be activated via event triggers whichmay be associated with user activities (such as mouse move or click,page transition etc.), activities associated with other users (such asan underlying database or a specific database record being updated byanother user), system events or other types of conditions.

The activated code may be executed inside the website building system'sclient element, on the server platform or by using a combination of thetwo or a dynamically determined execution platform. Such a system isdescribed in US Patent Publication No. US 2018/0293323 entitled “Systemand Method for Smart Interaction Between Website Components”, published11 Oct. 2018, commonly owned by the Applicant and incorporated herein byreference.

Typical site creation may be based on a number of models, including avisual editing model (in which the user edits a previously created site)and an automatic site generation model or a combination thereof, asillustrated in FIG. 1 to which reference is now made and is described inU.S. Pat. No. 10,073,923 entitled “System and Method for the Creationand Update of Hierarchical Websites Based on Collected BusinessKnowledge”, granted 11 Sep. 2018, commonly owned by the Applicant andincorporated herein by reference.

It will be appreciated that, throughout the specification, the acronymWBS may be used to represent a website building system. FIG. 1illustrates a system 100 that comprises a typical website buildingsystem 5 in communication with client systems operated by WBS vendorstaff 61, a site designer 62 (i.e. a user), a site user 63 (i.e. user ofuser) and with external systems 70. Website building system 5 mayfurther comprise a WBS (website building system) site manager 10, anobject marketplace 15, a WBS RT (runtime) server 20, a WBS editor 30, asite generation system 40 and a WBS content management system (CMS) 50.It will be appreciated that the elements of FIG. 1 may function asdescribed in U.S. Pat. No. 10,073,923.

In the visual editing model, the user (designer) edits a site based onone or more website templates. The website building system provider mayprovide multiple site (or other) templates, as described hereinabove.Users may have the option to start with an empty site (essentially a“blank page” template) but would typically start with an actual sitetemplate.

The website building system provider may provide site templates rangingfrom the very generic (e.g. mobile site, e-store) through the morespecific (e.g. law office, restaurant, florist) to the highly specificones (e.g. a commercial real-estate law office or a Spanish tapasrestaurant). Such templates are typically stored in a repositoryaccessible to users of the website building system and are typicallyclassified according to business type, sub-type or industry. Templatesmay also be created (and classified) according to style, color range orother parameters and not just according to business type. Site templatesmay be extended with additional (typically back-end) functionality,services and code in order to become full-fledged vertical solutionsintegrated with the website building system.

Thus, the user's first experience when creating a site using a websitebuilding system visual editor may typically be that the user chooses atemplate (e.g. according to style or industry type/sub-type), and thenedits the template in the visual editor, including the editing ofcontent, logic, layout and attributes. Such editing may include adaptingthe template and its elements to the details of the user's business. Theuser may then publish the modified site.

Under the site generation model, the website building system generatesan initial site for the user, based on a selected template, possiblymodified by filling-in common elements of information, and possiblyallowing follow-up editing of the generated site. This filling-in isrequired as various pieces of information (such as the business name ora description of the management team) are included in multiple locationsin the template's pages. Thus, the user may have to change the businessname (for example) in multiple places throughout the template.

Furthermore, some template elements (e.g. a generic product page) mayappear multiple times, with each instance displaying the details of adifferent instance of an underlying entity (e.g. different productsoffered in the site). Such multiple instances may be manually specified(e.g. the details of different persons in the company's management team)or dynamically derived from an external database (e.g. product detailsfrom the “products on sale” database). Such an arrangement is oftenknown as a “repeater”.

The template may also include fields. For example, the website buildingsystem may allow the template designer to specify fields (also known as“placeholders”) for the insertion of values inside the templates, suchas {CompanyName}, {ProductName}, {ProductPrice} etc. The user may alsospecify the values for the fields defined in the template selected forthe website.

The website building system may allow the user to enter simple orcomplex values (e.g. text and images), as well as additional (non-field)information, such as selection of included pages or web site areas,colors, style information, links, formatting options, website displayoptions, decoration elements (such as borders and backgrounds), etc.

The website building system may also allow the user to enter some ofthis additional information before selecting a template and may use thisinformation to help in selecting a template (e.g. by narrowing the setof proposed templates). For example, the user may select a certaingeneric color scheme (e.g. pastel colors) or style (e.g.business/formal), and the system may then use this selection to narrowthe set of proposed templates.

The system may also display a series of views or questionnaires to allowthe user to enter values or selections (for both the defined fields andthe additional information above). The system may further create aconnection (or binding) between a multiple-instance element of thetemplate (as described herein above) and an internal or externaldatabase which provides the data instances used to generate thedisplayed instances.

Once a template has been selected and its fields and additionalinformation have been specified (e.g. through the questionnaires orthrough binding to data sources), the website building system maygenerate the website containing the combined information. The user maythen publish the site (through the website building system orotherwise).

A website building system may perform semi-automatic site creation usinga different model as described in U.S. Pat. No. 10,073,923. Under thismodel, the system gathers information on the user and his web siterequirements from multiple sources which may include, for example:user-filled questionnaires; existing user presence (such as existing websites or social media presence), industry sources (such as general tradeweb sites), off-line information and internal system repositories whichprovide information on specific business types, such as basic templateinformation for specific business types (lawyers, restaurants, plumbers,graphic designers etc.), possibly refined for specific industries (e.g.distinguishing between real-estate lawyers and personal injury lawyers).

The system may also gather external information from other sites, bothinternal and external to the system. Such information may affect, forexample, the selection of offered questionnaires and layout elements,proposed defaults, etc. Such information may also typically be collectedon a statistical or summary basis, in order not to expose informationbelonging to any single user, and to protect users' privacy, anonymityand legal rights (such as copyrights). Such information may be locatedbased on information provided by the user which may be direct (e.g. anexisting website address) or indirect (a business name and geographicaladdress which can be used to locate information about the business).

The gathered information is analyzed and arranged into a repository ofcontent elements which are then mapped onto layout elements whichpresent the content from the content elements and combine the layoutelements to form the site. The layout element mapping, selection andcombination process may be fully automatic or semi-automatic (i.e.including user interaction).

To support the above-mentioned functionality above, a website buildingsystem will typically maintain a series of repositories, stored over oneor more servers or server farms. Such repositories may typically includevarious related repositories, such as a user information/profilerepository, a WBS component repository, a WBS site repository, aBusiness Intelligence (BI) repository, an editing history repository, athird-party application store repository, etc. The system may alsoinclude site/content creation related repositories, such as aquestionnaire type repository, a content element type repository, alayout element type repository, a design kit repository, a filledquestionnaires repository, a content element repository, a layoutelement repository, a rules repository, a family/industry repositoryetc. A description of these repositories may be found in U.S. Pat. No.10,073,923.

SUMMARY OF THE PRESENT INVENTION

There is therefore provided, in accordance with a preferred embodimentof the present invention, a website building system (WBS) and a methodimplemented thereon. The system includes a processor implementing amachine learning feedback-based proposal module and a database storingat least the websites of a plurality of users of the WBS and componentsof the websites. The module includes a plurality of per activity AIunits and a feedback system. Each unit supports one or more specificactivity related to the WBS and provides at least one system suggestionto users of the WBS related to its the specific activity. Each peractivity AI unit includes at least one machine learning model suitablefor the activity supported by its the per activity AI unit. The feedbacksystem provides a plurality of different kinds of feedback from theusers for updating the at least one machine learning model. The feedbacksystem analyzes the feedback to determine which of the at least onemachine learning models to update.

Moreover, in accordance with a preferred embodiment of the presentinvention, the feedback system includes an implicit feedback handler toanalyze at least editing histories of the users to determine whatfurther activity the users perform on their websites and/or within theWBS and to generate therefrom implicit feedback to train relevant themachine learning models.

Further, in accordance with a preferred embodiment of the presentinvention, the feedback system also includes an explicit feedbackhandler which analyzes at least user responses to the at least onesystem suggestion to determine how the users respond to the at least onesystem suggestion and which generates therefrom explicit feedback totrain relevant the machine learning models.

Still further, in accordance with a preferred embodiment of the presentinvention, the WBS also includes an editor operative with the proposalmodule. The tasks include at least one single component task within theeditor. The single component task can be image resolution improvement,face detection, portrait segmentation, objection segmentation, imagecropping, image enhancement, logo creation or site text generation.

Further, in accordance with a preferred embodiment of the presentinvention, the tasks include at least one multi-component task improvingsite function. The multi-component task can be component grouping,component group labeling, component ordering, object analysis, objecttransformation, desktop to mobile transformation, importation ofwebsites, template replacement, support of responsive editors oralternate design suggestion.

Still further, in accordance with a preferred embodiment of the presentinvention, each the per activity AI unit includes an interactiongenerator to provide at least one suggestion related to each theactivity to the users based on the output of the at least one machinelearning model.

Moreover, in accordance with a preferred embodiment of the presentinvention, wherein the at least one machine learning model is a modelsuited to the task and selected from one or more of the following typesof models: supervised, unsupervised, prediction algorithms,classification algorithms, clustering algorithms, associationalgorithms, time-series forecasting algorithms, image to image models,sequence to sequence models, and Generative models.

Further, in accordance with a preferred embodiment of the presentinvention, the feedback system also includes at least one of a responseevaluator to evaluate a response quality of feedback responses from theusers, a user evaluator to evaluate a user quality in giving feedback, avendor handler to analyze feedback at least from vendor staff of theWBS, and a community handler to analyze feedback at least from acommunity of users.

Still further, in accordance with a preferred embodiment of the presentinvention, the proposal module updates at least one of the machinelearning models periodically and/or based on user activity. The useractivity can be whenever a user makes a change to the website orwhenever the user publishes the website.

Moreover, in accordance with a preferred embodiment of the presentinvention, the explicit feedback handler receives a plurality of objecttypes from a plurality of interaction formats.

Further, in accordance with a preferred embodiment of the presentinvention, the implicit feedback handler receives information gatheredfrom within the WBS. The information can be disposition of the componentand/or business information, user information, and site information.

Moreover, in accordance with a preferred embodiment of the presentinvention, the at least one machine learning model is multiple modelsfor multiple WBS tasks. In one embodiment, a first one of the multiplemodels interacts with a second one of the multiple models, such as thefirst model assists the second model or the first model providestransfer learning to the second model.

Further, in accordance with a preferred embodiment of the presentinvention, a first one of the multiple models receives input from adifferent population of users than a second one of the multiple models.For example, the population is defined as being per country, percommunity, per group, or per user profile.

Still further, in accordance with a preferred embodiment of the presentinvention, the feedback system infers a user profile of a particularuser from one of the editing history of the particular user andinformation about the website of the particular user.

Moreover, in accordance with a preferred embodiment of the presentinvention, the interaction generator provides one of a plurality ofinteractions as a function of parameters of the user, of the website ofthe editing history.

Further, in accordance with a preferred embodiment of the presentinvention, the user quality is defined as a function of at least one of:the content of the feedback, a profile of the user, the editing historyof the user and at least one previous score of the user for previousresponses.

Still further, in accordance with a preferred embodiment of the presentinvention, the user evaluator disqualifies the user from providingfurther feedback if the user quality is lower than a predefinedthreshold.

Moreover, in accordance with a preferred embodiment of the presentinvention, when the at least one single component task is imagecropping, its machine learning model is an image cropping model whichinfers that areas of an image covered by overlapping components arenon-important areas of the image.

Finally, in accordance with a preferred embodiment of the presentinvention, the WBS also includes a site generation system which receivesindications of non-important areas of a background image from at leastone associated per activity AI unit and places layout elements over thenon-important areas.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIG. 1 is a schematic illustration of a prior art website buildingsystem;

FIG. 2 is a schematic illustration of a system which integrates onlinemachine learning feedback into website building system services,constructed and operative in accordance with an embodiment of thepresent invention;

FIG. 3 is a schematic illustration of the elements of a machine learningfeedback-based proposal module forming part of the system of FIG. 2;

FIG. 4 is a flowchart illustration of a typical user workflow for systemof FIG. 2;

FIG. 5 is a schematic illustration of the training and continuing setupof one per activity AI unit forming part of the module of FIG. 3;

FIG. 6 is a flow chart illustration of an exemplary method implementedby one type of machine learning model forming part of the AI unit ofFIG. 5, for the task of layout understanding and component grouping;

FIG. 7 is a schematic illustration of the elements of an editing taskhandler forming part of the module of FIG. 3;

FIG. 8 is a schematic illustration of the elements of a site functionupdater forming part of the module of FIG. 3;

FIGS. 9A, 9B and 9C are flowchart illustrations of alternative methodsfor component grouping using ML models based on computer vision, basedon triplet analysis, and based on a combination of triplet analysis andcomputer vision, respectively;

FIG. 10 is a pictorial illustration of a hierarchical grouping processperformed on a page;

FIG. 11 is a schematic illustration of the hierarchy of the page of FIG.10;

FIGS. 12A, 12B, and 12C are pictorial illustrations of group labels forthe same web page defined at three levels of hierarchy;

FIGS. 13A and 13B are illustrations of a labeling user interface usefulin understanding a labeling process for grouping; and

FIG. 14 is a pictorial illustration of how a given layout can bedisplayed on multiple available screen widths.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

Applicant has realized that the website building process may bechallenging for the novice builder or designer, especially those withlimited technical knowledge. Novice designers may face problems both atthe layout level (such as the arrangement of objects on pages) and atthe editing level (such as the enhancement of an image). Applicant hasfurther realized that even the most expert website designer may also bechallenged by certain tasks, such as image enhancement and logo creationor other functionality that cannot be performed manually.

Applicant has also realized that the above mentioned challenges may beovercome by having a website building system that provides thedesigner/WBS user with a set of machine learning/artificial intelligencebased services to support the website building and maintenance workflow(such as support for image cropping for images to be used in thewebsite). The WBS may employ different types of feedback to trainunderlying machine learning models using different object types,feedback types and interaction formats, as well as gathered informationavailable from within the website building system and informationexternal to it which may be analyzed accordingly. The results of thesedata gathering/analysis/feedback interactions may then be integratedinto the site design workflow.

Applicant has further realized that the WBS may employ specific modelsto determine from the results of the above mentioned analyses whetherfeedback from a specific user (or even the user himself) are trustworthyand whether it is necessary to weight the feedback response accordinglybefore it is used.

In the realm of website building systems, machine learningmodels/artificial intelligence may provide diverse solutions. Websitedesign, creation, and maintenance involve a variety of tasks. Thesetasks are at different levels of abstraction and deal with objects ofdifferent types. Some of these tasks deal with media files (e.g.,images, audio, and video) or text files/objects, while some deal withhigher-level objects (such as the design and layout of a page section, asingle page or a collection of pages). Some are highly mechanical ortechnical, while some involve a high degree of creativity or skill.

Applicant has realized that machine learning models should be designedfor each particular activity and may be initially trained using vendortraining data and enriched further using feedback from both implicit andexplicit data from and about the WBS user (the designer) and hiswebsites. Implicit feedback may include designer behavior information(editing history, business intelligence, general user info, etc.) andexplicit feedback may be based on questions posed to the designer aboutsuggestions or proposals (which suggested picture of the Taj Mahal isclearer?) at any appropriate point during website design, creation ormaintenance. The models may be standalone, i.e. task specific only, ormay act as sub-routines for other models.

Applicant has further realized that functionality and tasks involvedwith the automated processes of site creation (such as site generation,site import, conversions from other systems, conversions betweendifferent display platforms, etc.) may also be improved using the samedata gathering/analysis/feedback interactions.

The use of feedback from the user to improve the performance of machinelearning models is known in the art and is one form of reinforcementlearning. One example is the classification of e-mails into spam andnon-spam e-mails (such as US Patent Publication US20170222960A1 byMicrosoft Technology Licensing LLC). In the case of spam e-mailclassification, the user feedback is typically fairly simple; an e-mailcan be classified into spam or non-spam (or maybe as “light spam” insome systems, i.e., spam e-mail which is not as obtrusive and irrelevantas most spam e-mails are). The simplicity of the response makes iteasier to get user feedback than cases which require more complexfeedback.

Another common example is web search engines, which track the actualsearch results selected by the user and use it to refine furthersearches. The results selected by the user provide training data for thesearch engine's machine learning models.

The machine learning tasks in the present invention involve editing orotherwise transforming a given object X to create a new or modifiedobject Y. For example, taking an image X and transforming it into amodified image Y through editing, cropping, attribute change(brightness, contrast, etc.), quality improvement, etc.

The resulting object Y may be much more complex than a modified versionof an image and may be an object or a set of objects which are verydifferent from the object X. For example, in an object segmentation taskapplied to an image X, the result of the task may be a composite Zcomprising a version of the image X which shows only the physicalobjects (detected in image X) over a modified background. However,composite Z may also comprise a set of high-level product details (e.g.based on matching the detected physical objects against an e-commerceproduct catalog) including such elements as object outline coordinatesin the original image, product metadata, product classification, etc.

Another category of tasks may involve suggesting a given object orelement to be added. This may be purely system initiated or based onuser interaction. For example, the WBS may suggest an image (e.g. from arepository of system or other stock images) which is a good fit to theuser's text in a certain site section. Conversely, the WBS may suggesttext to fit an image. Thus, the WBS may add web-elements and not justedit existing ones.

As another example, the WBS may provide the user with the ability toperform template replacement. Users (designers) often build their sitebased on a template provided by the WBS vendor. The user may perform asubstantial modification to this underlying template, including designand layout changes, insertion and removal of pages, page elements andsite sections as well as the entry of user-specific site text, media,and data. The user may later desire to switch to a different template,which would require the user to map the changes (layout and content) tothe new template and reapply them as appropriate. The machine learningtools of the present invention may provide high-level editing tools toperform this task and also automated tools and machine learning-basedtools as described herein below. In such a case, the object beinghandled is not a mere image (or other media file) but an entire site.

It will be appreciated that the WBS may provide a variety of tools toperform these tasks, ranging from simple task-specific tools (e.g.,image cropper, image attribute modifier) to media editing tools (e.g.,an image editor) to full-scale page editing or similar tools (templatereplacement applier). These tasks may occur in various stages of thesite creation workflow, e.g. at a preliminary media gathering anduploading stage, just before the site/page editing, during or inconjunction with or integrated with the site or page editing, or as partof the post-editing or publishing process.

Reference is now made to FIG. 2 which illustrates a system 200 whichintegrates online machine learning feedback into website building systemservices, constructed and operative in accordance with an embodiment ofthe present invention. As discussed herein above, for WBS systems, thereare a variety of tasks which may benefit from machine learning models,and each of these models may, in turn, benefit from user feedback.System 200 may integrate feedback loops into the workflow of the WBS soas to make it easy (and desirable) for the user to provide the requiredresponse. In addition, the feedback loops may extract relevant feedbackinformation from user activities. System 200 may aim to make the userfeedback interaction a part of the regular WBS interaction required bythe system in order to complete the required task.

System 200 may comprise the elements of system 100 as described in U.S.Pat. No. 10,073,923 (as well as any sub elements) in conjunction with anML feedback-based proposal module 300. ML feedback-based proposal module300 may interface with multiple parts of website building system 5 and,in particular, with WBS editor 30, site generation system 40 and CMS 50as described in more detail herein below.

It will be appreciated that the following discussion refers toapplications created by designers using WBS 5 and accessed by theend-users as websites, although system 200 may be applied to othercategories of on-line applications which are accessed using specificclient software (proprietary or not). End-users may access these websites using client software on regular personal computers and also onsmart-phones, tablet computers and other desktop, mobile or wearabledevices. Alternatively, system 200 may be applicable to systems whichgenerate mobile applications, native applications or other applicationtypes as described in U.S. Pat. No. 9,996,566 entitled “Visual DesignSystem for Generating a Visual Data Structure Associated with a SemanticComposition Based on a Hierarchy of Components”, granted 12 Jun. 2018,commonly owned by the Applicant and incorporated herein by reference.

Furthermore, the following discussion focuses on websites hosted by thewebsite building system provider. It will be appreciated that thefollowing discussion refers to the handling of imported images and theirintegration into an edited site. However, system 200 may handle otherobjects or object sets, such as images with additional information(cropping, embedded objects, identified faces/portraits/objects, etc.),text objects (fields or embedded text-type objects), other components,component collections, page layout, complete web pages, and completesites.

Reference is now made to FIG. 3 which illustrates the elements of MLfeedback-based proposal module 300. It will be appreciated that MLfeedback-based proposal module 300 may enhance all forms of editingoperations and WBS functionality by proposing possible enhancements tothe current task of WBS user 61 or 62, where these enhancements may bedrawn from previously received feedback from the community of WBS users61 or 62. Module 300 may comprise one or more per activity AI units 310(as described in more detail herein below), each specifically designedfor its associated editing task or WBS system task. Each per activity AIunit 310 may be a module for enhancing a particular editing task or WBSsystem task and may generate a specific interaction UI for thatparticular activity to which WBS user 61 or 62 may respond. Each peractivity AI unit 310 may comprise an interaction generator 315 and atleast one ML model 317, trained for the particular activity or task.Alternatively, multiple per activity AI units 310 may share oneinteraction generator 315 and/or units 310 may comprise multiple MLmodels 317 and explicit feedback handler 320 may also operate with oneof the interaction generators 315.

ML feedback-based proposal module 300 may additionally comprise anediting task handler 370 to provide the task specific suggestions fromthe relevant AI unit 310 to the WBS user 61 or 62 for single objectoperations and a site function updater 400 to provide such suggestionsfor multiple object operations. Each of editing task handler 370 andsite function updater 400 may communicate with either WBS editor 30 orsite generation system 40, as appropriate and as described hereinbelowwith respect to FIGS. 7 and 8.

In addition, ML feedback-based proposal module 300 may comprise afeedback system 305 comprising an explicit feedback handler 320, animplicit feedback handler 340, a community handler 380, and a vendorfeedback handler 390 to provide user feedback to ML models 317 toimprove their functioning, based on what WBS users 61 and 62 have done.ML models 317 may be updated or retrained at any suitable time, such asduring operation, periodically, when triggered, such as when a userchanges or publishes his/her website.

As described in more detail hereinbelow, feedback system 305 may analyzethe feedback received from users to determine which machine learningmodels to train.

Explicit feedback handler 320 may further comprise an explicit feedbackanalyzer 325 and a user analyzer 328. Implicit feedback handler 340 mayfurther comprise an implicit feedback analyzer 345. The functions ofthese elements are discussed in more detail herein below. Alternatively,though not shown in FIG. 3 for clarity purposes, analyzers 325, 328 and345 may operate within per activity AI units 310, particularly when theyare very specific to the activity being reviewed by the specific peractivity AI unit 310. As discussed herein above, system 200 may usemachine learning models to support the user in performing various tasks.System 200 may integrate user feedback gathering (both explicit andimplicit) into the WBS workflow and into the various tasks themselves.System 200 may utilize provided feedback together with additionalinformation known to the WBS to improve the ML models 317. Suchinformation may include current and historical information regarding theuser, his or her media, and his or her website(s), as well asinformation related to additional users and sites. Such information mayinclude (for example) parameters of the user (geography, experience,professional design status, etc.), the user's web site(s) parameters(e.g., the template used or hints from the template) and additionalgathered information. This information may be stored in any of therepositories in CMS 50 as described in more detail herein below.

Reference is now made to FIG. 4 which illustrates a typical userworkflow for system 200. In stage 1 (user preparation), the WBS userselects an object (such as an image, layout page or otherwise) to behandled in a given task, typically via WBS editor 30. Alternatively, theuser may upload an image to be used (after editing) in the currentwebsite. The result is a selected object A for handling.

In stage 2 (system suggestion), editing task handler 370 or sitefunction updater 400 may receive object A and may provide it to therelevant AI unit 310 whose ML model 317 may analyze it to determine aset of “suggestions” B1-Bn which may improve upon object A (or mayprovide processing parameters or additional information as furtherdiscussed below). It will be appreciated that there could be multipleforms for this suggestion, as discussed in more detail herein below. MLmodel 317 may also suggest adding an appropriate element, such as animage from an image repository in CMS 50, generated text, etc.

In stage 3 (user feedback interaction), interaction generator 315 maydisplay object A along with suggested version(s) B1-Bn to the user, viathe relevant handler 370 or 400. The user then determines or derives aversion C through interaction with handler 370 or 400. Thisdetermination or derivation provides explicit feedback to system 200.

In stage 4 (further editing, interaction and site design), the userfurther edits the version C (if required) to create a version D which isintegrated into the site S to be published by site generation system 40.This further editing provides implicit feedback to system 200. Suchediting may include editing of the object itself (version C), thedisposition of the object (whether or not the object was integrated intothe web site and in what way), processing done on the object,information extracted from the object or other editing related to theobject (even if not directly modifying it).

It will be appreciated that suggestions B may not be limited to anactual modified object (or set of objects). The output could alsoinclude, for example, a set of parameters used to process A so as tocreate modified object B. For example, an image enhancement task AI unit310 may propose values for specific filters (e.g., brightness, contrast,etc.) rather than new image B itself. The user may then modify theseparameters. Similarly, in an image cropping task (or varioussegmentation tasks), the associated AI unit 310 may provide the croppingparameters (X/Y ranges) which the user may modify, rather than providingthe resulting cropped image(s).

Suggestion B may also be a set of specific editing steps (orinstructions) which may be applied to object A in order to createmodified object B. System 200 may allow the user to modify these steps,rather than to provide the result of their execution and per activity AIunit 310 itself may generate these steps instead of a completedsolution. When handling editing steps, ML feedback-based proposal module300 may employ machine learning models from the realm of naturallanguage processing and natural language generation, which may providebetter results on sequences of steps. Such natural language handlingmodels may be used in addition to or instead of the regular models.

It will be appreciated that the suggestion and feedback interactions instages 1 and 2 may take multiple forms. For example, ML feedback-basedproposal module 300 may suggest B, which may be, for example, aretouched version of an image. The user may accept or reject thesuggested B. The derived version C is then either B (if the change wasaccepted) or A (if the change was rejected).

In another example, interaction generator 315 may display B togetherwith a set of parameters for modification or other processing of A(e.g., cropping rectangle coordinates, face-enclosing rectanglescoordinates or brightness filter setting). The user may accept theseparameters (and the suggested B) or may modify these parameters (e.g.,move/resize the cropping rectangle) to create new version C. MLfeedback-based proposal module 300 may also apply model-based generationof a series of processing steps or instructions.

ML model 317 may also suggest a modified version of A (e.g., a retouchedversion of an image). In this scenario, system 200 may provide aspecific editor which allows the user to modify B further to create apreferred version C.

ML feedback-based proposal module 300 may also generate (and offer)multiple versions B1 to Bn from which the user may select one (todetermine a version C). ML feedback-based proposal module 300 may alsoallow the user to reject all options (in order to generate additionaloptions), to merge options, or to specify different version generationparameters (and re-run stage 1 to generate a new set of solutions).

ML feedback-based proposal module 300 may utilize both implicit feedbackand explicit feedback, as discussed hereinabove, or only implicitfeedback or only explicit feedback, as appropriate.

It will be appreciated that system 200 may also combine the feedbackinteraction options above allowing the user (for example) to accept,reject, or edit a given suggested solution (or set of solutions). System200 may also provide specialized editing tools or environments which areaware of the existence of multiple “layers” (original A, suggested B andcurrently edited C) or multiple solutions (B1 . . . Bn). The feedbackinteraction tools or environment may not be limited to a simple editingenvironment (for image/layout editing or otherwise) and may interactwith the original per activity AI unit 310 which produced B or mayemploy additional auxiliary models for specific feedback interactiontasks.

For example, system 200 may offer (for image-related tasks) the use ofspecific brushes or area marking tools which allow some parts of thesuggestion B (as defined by brush shape/mask or marked area) to revertto the pre-model A values i.e. essentially an “undo brush.”

Conversely, system 200 may offer similar brushes or area marking toolswhich may be employed on a copy of A to apply the model-suggestedsolution B to specific areas i.e. essentially a “redo brush.” Such abrush may be useful, for example, when the user is generally unsatisfiedwith the proposed suggestion B, but still wants to use specific parts ofB when creating C.

Similarly, system 200 may offer the option to mark specific areas of Aand to activate or apply per activity AI unit 310 specifically on them.Such an option could be used, for example, in an object recognition taskin which the relevant per activity AI unit 310 failed to detect objectsin some areas. This option may enable the user to activate the relevantAI unit 310 on user-marked areas, which may be used in addition to orinstead of the suggested solution (fully or in a specificarea)—providing further hints (which serve as training information) toAI unit 310. System 200 may use the original model, may use the originalmodel in conjunction with some specific execution parameters, or may usea different model which may be particularly suited to detecting theobjects in the user-marked areas.

When reviewing multiple offered layouts, system 200 may offer tools tomerge sub-areas from different models. System 200 may use a specificmachine learning model for merging multiple layout elements into onecoherent layout and may also use dynamic layout technology to do so,such as is described in U.S. Pat. No. 10,185,703, commonly owned byApplicant and incorporated herein by reference.

It will be appreciated that system 200 may also offer comparison tools,allowing the user, for example, to overlay two or more of the objectversions (A, B or B1 . . . Bn and current C) and flip between them orotherwise visually compare them, to help him edit C.

System 200 may provide multiple different feedback interactions (or userinterfaces) to different users or may provide multiple interactions withthe same user. This could depend on the object itself (e.g., simple vs.complex object), on results from interaction generator 315 (i.e. thesuggested solution(s)), on the user parameters (e.g., by level ofexperience of user, number of times the user performed this specifictask, etc.), on the site parameters or on other WBS parameters.

System 200 may then re-train the per activity AI unit 310 using the pair[A, C] as an example of the desired result C based on the originalobject A, or using the parameters used to generate C as in theparameterized filters example above. Later, system 200 may gatheradditional information (including, for example, D's information, WBSinformation, and user activity information) and may use this informationfor additional training as discussed in more detail herein below.

As an alternative, instead of training existing models with additionaltraining data, system 200 may generate new models (based on the gatheredtraining data) and may evaluate their performance against the existingmodels as discussed in more detail herein below.

ML models 317 may be any suitable model for the task or activityimplemented by each per activity AI unit 310. Machine learning modelsare known in the art and are typically some form of neural network. Theterm refers to the ability of systems to recognize patterns on the basisof existing algorithms and data sets to provide solution concepts. Themore they are trained, the greater knowledge they develop.

ML models 317 may be learning models (supervised or unsupervised). Asexamples, such algorithms may be prediction (e.g., linear regression)algorithms, classification (e.g., decision trees, k-nearest neighbors)algorithms, time-series forecasting (e.g., regression-based) algorithms,association algorithms, clustering algorithms (e.g., K-means clustering,Gaussian mixture models, DB scan), or Bayesian methods (e.g., NaïveBayes, Bayesian model averaging, Bayesian adaptive trials), image toimage models (e.g. FCN, PSPNet, U-Net) sequence to sequence models(e.g., RNNs, LSTMs, BERT, Autoencoders) or Generative models (e.g.,GANs).

Alternatively, ML models 317 may implement statistical algorithms, suchas dimensionality reduction, hypothesis testing, one-way analysis ofvariance (ANOVA) testing, principal component analysis, conjointanalysis, neural networks, support vector machines, decision trees(including random forest methods), ensemble methods, and othertechniques. Other ML models 317 may be generative models (such asGenerative Adversarial Networks or auto-encoders) to generate solutionelements (such as website building system elements and media objects).

For most embodiments, ML models 317 may undergo a training or learningphase before they are released into a production or a runtime phase ormay begin operation with models from existing systems or models. Duringa training or learning phase, ML models 317 may be tuned to focus onspecific variables, to reduce error margins, or to otherwise optimizetheir performance. ML models 317 may initially receive input from a widevariety of data, such as website building system CMS data, siteinformation (including components, structure, layout, formatting andother information), external information, user information, gatheredediting history, gathered business information, etc.

In another embodiment and when appropriate for the particular task, oneor more of ML models 317 may be implemented with rule-based systems,such as an expert system or a hybrid intelligent system whichincorporates multiple AI techniques. These are discussed in the articlesin the Wikipedia entitled “Rule-Based System” and “Hybrid IntelligentSystem”, at en.wikipedia.com.

During runtime, the trained ML models 317 may provide proposals forimprovements that their associated interaction generators 315 mayprovide to the WBS users 61 or 62. As mentioned hereinabove, at someappropriate time, ML models 317 may be updated with information from anyor all of feedback handlers 370, 340, 380 or 390.

Reference is now made to FIG. 5 which illustrates the training andcontinuing setup of one per activity AI unit 310. As discussed hereinabove, each machine learning model 317 may be per activity and ML models317 may have sub-models and may interact with each other. The models 317may also be trained with different levels of data, such as data from allusers (“universal data”), data from a community of users (“communitydata”) or data from a single user. It will be appreciated that theexistence of multiple models 317 for multiple tasks and/or for multiplegroupings of users allows for multi-task learning, benefiting from ananalysis of commonalities and differences between the different tasks.Furthermore, the models 317 may support each other, for example, facedetection and object detection models may provide support (including viatransfer learning) to an image cropping model or to portrait/objectsegmentation models. This interaction between the models 317 may alsoallow system 200 to provide supporting information or explanations toits proposed solutions, e.g., showing the underlying detected face andobjects which support a given image cropping solutions, as described inmore detail herein below.

Reference is now made to FIG. 6, which illustrates an exemplary methodimplemented by one type of ML model 317, for the task of layoutunderstanding and component grouping. ML model 317 of FIG. 5 may berecurrent neural network (RNN) or a long short-term memory (LSTM) basedand may initially extract (step 210) web elements to be processed. Instep 212, ML model 317 may extract the relevant features of the webelements or components, such as their geometric properties (position onthe page, size and depth), type and other metadata (text font and size,image embedding, content (or parts thereof), links to other components,template information, editing history, associated code, etc.).

These features are then processed by a multi layered RNN/LSTM model inorder to produce embeddings for each component. These embeddings arethen clustered (step 214) into groups. Pages may be labelled by manuallygrouping elements which are required to remain together when renderingthe site in a new aspect ratio (for example, mobile view) or a differenttarget display. These labelled pages comprise the ground truth withwhich the ML model 317 is trained via supervised learning, in order tosuccessfully identify groups of elements. The RNN/LSTM model is trainedusing a contrastive or triplet loss function, whereby the ground truthgroup labels are processed into an adjacency matrix (target signal) andthe output embeddings of the model are used to generate a distancematrix between element pairs (predicted signal). Since each site isdifferent, in step 216, the resultant layout definitions (or otherclassifications) may be clustered with a clustering model, to define thebest groups to use for the site it is operating on.

It will be appreciated that system 200 may employ multiple sources oftraining data and feedback as is illustrated in FIG. 5 (back to whichreference is now made) to support the construction and training ofmachine learning models for various WBS tasks. These can be used for theinitial training of the models, as well as for on-going, on-linetraining. Training data may include explicit training by WBS vendorstaff 61 (e.g. through an interactive UI) and training via mass dataimport (though an appropriate import interface). This importing could befrom external data sources (such as an existing repository of images inCMS 50 associated with matching cropping area definitions). It couldalso be training data derived from existing user material within the WBS(for example images used in the WBS together with their cropping areadefinitions as made by the users). Implicit feedback handler 340 mayprovide each ML model 317 with this data as described in more detailherein below.

Initial training may also come from staff external to WBS vendor staff61, such as hired external staff (possibly matter experts/professionals)or crowd-sourced staff (using a crowd-sourcing platform such as Amazon'sMechanical Turk).

Training may also come from existing users of the WBS through system200, in the form of initiated interactions which request specificfeedback on specific data samples as handled by explicit feedbackhandler 320. These may be data samples selected specifically to trainthe particular ML model 317. System 200 may also employ active learningtechniques to select interaction strategies which provide the highesttraining value and implicit feedback analyzer 345 may also analyze userinformation and user web site information to detect the best users toquery, as described in more detail herein below. It will be appreciatedthat implicit feedback analyzer 345 may consider the informationmaintained about past responsiveness, performance, and answer quality ofthe user. The analysis may also take into account the proficiency of theuser in executing the specific task (for which the specific model isbeing trained for). Such a proficiency analysis could be based (forexample), on an analysis of the user's website(s). Further implicitfeedback may include other activities within WBS 5, such as asave/discard decision, a publish action, designer site browsingpatterns, etc.

System 200 may also incentivize the user to provide feedback withmonetary or other bonuses, such as a premium package upgrade for usewith system or other in-system goods (e.g., enhanced web site templates,higher capacity limitations, etc.).

It will be appreciated that the abovementioned sources are in additionto the training information gathered in general by implicit feedbackhandler 340 and explicit feedback handler 320 as described in moredetail herein below. It will be further appreciated that for thetraining cases discussed above, vendor staff 61 providing their trainingmay provide additional labeling or feedback information which would notbe asked for as part of a regular feedback interaction.

As discussed herein above, system 200 system may extract feedbackinformation from additional sources besides the user feedbackinteraction itself, and such information may be more indicative,representative, or otherwise appropriate than the regular feedbackprovided by the user. This inferred feedback will be referred to as“implicit feedback” as opposed to the explicitly provided “explicit”feedback. It will further be appreciated that implicit feedback may begenerated by implicit feedback handler 320 at any appropriate time, suchas periodically, or whenever a user makes a change to the website, orwhenever the user publishes the website, etc. Implicit feedback handler320 may gather the changes, analyze them and then provide them to therelevant AI units 310 to update their models appropriately.

For example, system 200 may support the user when importing an image byoffering an enhanced version of the image using an image enhancing MLmodel 317, which may provide automatic enhancing of image brightness,contrast, etc. The user may be asked if he prefers the original or theenhanced image, and the user's decision may be used to train the model.

Alternatively, system 200 may provide the user with specific editingtools to modify the applied brightness, contrast, and other changes(also known as “filters”). System 200 may provide model-based initialsettings for these editing tools, and the user may modify thesesettings. The user-modified settings may then be used as user feedbackto train image enhancing ML model 317.

However, the user may continue editing the site, and may further modifythe imported image during on-going site editing. He may also re-importthe original image, modify some of the parameters (e.g., through animage editor built into WBS editor 30) or even import a different imagealtogether (i.e., drop the original imported image). System 200 mayanalyze the new image and may use it to provide additional or updatedtraining information for the relevant model 317. For example, implicitfeedback analyzer 345 may analyze the level of brightness in the finalmodified image vs. the initial image selected (or the initial brightnessvalue selected) and may use this information about the change inbrightness to re-train image enhancing ML model 317. Implicit feedbackhandler 340 and its analyzer 345 may also extract additional featuresfor training not directly related to the image content, e.g., was theimage used on an important page (e.g., the site home page), aless-important page, or not at all? Similarly, implicit feedbackanalyzer 345 may analyze the placement of the image on the page in whichit appears to determine its importance (e.g. “above the fold,” “belowthe fold” or as an optional image). All of these may serve as modelfeatures for ML models 317.

As another example, at the appropriate time, explicit feedback analyzer325 may process an existing page A to determine a suitable improvedlayout B for the page (e.g., by modifying the arrangement, sizes, andtypes of components). The analysis may involve reviewing the componentsof the selection, the multiple possible layouts (e.g. B1-B4) presentedto the user and the user's final selection (e.g., B3). The user'sexplicit choice is the explicit feedback. Explicit feedback analyzer 325may utilize all explicit feedback or only some of it and may also mergeor summarize the changes to provide better input for retraining MLmodels 317.

As mentioned herein above, the user may later edit the “accepted” Cversion (which was B3), performing additional changes to create a laterversion D of the page. Implicit feedback analyzer 345 may track theselater changes and may use the D version as an additional source offeedback to re-train the relevant ML model 317. If the user edits pageB3 before accepting it, then explicit feedback analyzer 325 may analyzethat information as implicit feedback.

It will be appreciated that implicit feedback analyzer 345 may analyzethe differences between the C version of the page (the B3 versioninitially selected by the user) and the D version (the one finallypublished) and may isolate specific differences relevant to the modeland the features used by it. Such an analysis could include (forexample) a component difference analysis, e.g., using the differencesfor training changes to components which exist in B3. Such an analysiscould also include attribute-specific analysis, i.e., only take intoaccount attributes such as position and size, and not take into accountother attributes. Implicit feedback analyzer 345 may then provide theresults of its analysis to the relevant ML model 317 for retraining.

It will be further appreciated that such analysis can also be performedby implicit feedback analyzer 345 for other types of tasks and modelsavailable within the WBS.

Implicit feedback may also be gathered from sources external to thesite, such as feedback generated for other sites of the same designer(user) or sites of similarly-situated users (e.g., professionalgraphical designers in Japan) as discussed in more detail herein below.

It will be further appreciated that implicit feedback may be collectedat various points during the site creation workflow. It may be gatheredonline, i.e., immediately when the user modifies a site element whoseconstruction involved machine learning model-based editing or it may begathered upon a specific event, e.g., each time a handled site elementis edited or replaced.

Implicit feedback may also be gathered at the end of an editing session,assuming that the user has created a more finalized version of the webpage which better reflects the user's understanding. This assumption maynot be correct in some cases, as the user may have ended his editingsession without finalizing the page (or a given object in it), intendingto continue the work in the next editing session.

It may also be gathered when the site is published as this is likely tobe a “final” version of the web page and the objects in it (though sitesare often published multiple times in multiple versions). For example,in the improved page layout example above, this could very well be thebest point to sample the implicit changes. In this scenario, implicitfeedback gathering may help train the model with the right value even ifthe user (for example) changes his mind.

System 200 may also gather implicit feedback for multiple tasks andmodels simultaneously, as a given site may contain multiple objectsgenerated using multiple ML models 317. Further editing (which generatesfeedback) may involve multiple such objects.

It will be appreciated that for many types of systems and tasks, auniversal ML model 317 may be applicable. This could be, for example,when the decisions made by all users in the target audience of thesystem are similar. An example is the classification of e-mail into spamor non-spam, which would typically be uniform for all people reviewing agiven e-mail. In this situation, it would be rare for one person toclassify a given e-mail as spam and another person as non-spam.

Such a model may be used for all users but may be enriched withadditional model features which provide sufficient differentiationbetween different user categories. For example, features involving theuser's language and the e-mail language may be highly relevant for spamdetection, as unprompted e-mail in a language different from the user'slanguage is very likely to be spam.

It will be appreciated that generally, for many of the tasks performedin WBS systems, the desired result is not universal. The desired resultfor a given user may depend heavily on local or personal preference,community-related design preferences (based for example, on the country,region, designer, professional experience/expertise, industry, etc.) Forexample, professional and non-professional designers often havedifferent preferences regarding how some of the tasks (such as sitedesign or image enhancement) are to be performed. As another example,the preferences of a designer specializing in law office websites may bevastly different from the preferences of a designer specializing inmusical performance websites.

Thus, training a single universal per activity AI unit 310 (for anyspecific task) with the user feedback may not provide optimal resultsfor all users, and it may be better to train separate models forcommunities whose preferences are completely disjoint.

However, system 200 may use a universal per activity AI unit 310, with auniversal ML model 317, for some tasks. The universal AI unit 310 mayuse additional information available in the WBS, i.e. system 200 maytrain the universal ML model 317 based not just on the user's feedbackbut also using additional existing or analyzed WBS information (asdiscusses herein above in relation to implicit feedback analyzer 345).Implicit feedback analyzer 345 may also extract features based on userparameters (geography, experience, professional design status, etc.),the user's web site(s), the user activity, and additional gatheredinformation (as available in WBS CMS 50 or otherwise). The pertinent peractivity AI unit 310 would be best in handling cases that are common toall communities/users.

System 200 may also use additional multiple per-group or per-communityML models 317 for different populations (e.g., for all professionaldevelopers, for all lawyer website designers, for designer specializingin Japanese-style design, etc.). System 200 may train the pertinent MLmodel 317 and the relevant per-group ML model 317. The training data mayinclude the gathered user feedback as well as additional WBS information(as described herein above). System 200 may also use personal (per userand per-task) models 317 for tasks whose solutions are highlyindividual.

The groups may, in fact, overlap, and thus system 200 may train multiple(group-specific) models for each given gathered feedback. Communityhandler 380 may initially split the training flow (i.e. send thetraining data to multiple models) and afterward, may unite therecommended solutions generated by the multiple ML models 317 inmultiple ways, e.g., creating merged solution(s), selecting the bestsolution(s) of these offered by the multiple per activity AI units 310,etc. The group definitions may be unique for a given task (and itsrelated model) or may be shared between multiple tasks.

Community handler 380 may allow the groups to be managed manually (i.e.,adding and dropping group definitions and associated models asrequired). Community handler 380 may alternatively automaticallyidentify sub-segments of the general population which have similarpreferences (based on a separate model or other techniques such asclustering analysis). To determine which users belong to whichcommunity, community handler 380 may infer a user profile of aparticular user from the user's editing history and/or information aboutthe user's website. The user profile may also include businessintelligence about the user's website. For example, an art web siteaccessed mostly from Japan is most likely (though not necessarily) to befor Japanese-style art.

Community handler 380 may also expose some group definitions to the usercommunity and may furthermore allow a user to declare his affiliationwith one or more groups. For example, system 200 may support a “Baroquedesign style” group (with an associated per activity AI unit 310) andmay allow users who desire Baroque design style recommendations to jointhis group. In addition, community handler 380 may avoid exposing somegroups, e.g. “professional user” and “non-professional user” groupswhich are automatically assigned to the user based on a declared statusor analysis of the user's sites.

As discussed herein above, the integration of feedback into the workflowof the WBS improves the probability of the user providing feedback to aspecific model and improves the quality of the provided feedback. Thisimprovement results from the integration of either explicit or implicitfeedback.

In particular, for some tasks, the feedback interaction may besufficiently integrated with the WBS workflow so that the user providesthe feedback transparently when executing the task (e.g., by acceptingor rejecting an offered solution). Thus, the response rate to such afeedback interaction is likely to be as close to 100% as possible, andthe user is likely to provide a correct response (for the user'spreferences and knowledge at that time).

However, in some cases, users may fail to provide feedback or mayprovide incorrect feedback. For example, with an image enhancement taskin which the relevant ML model 317 may provide the initial setting forimage filters, the user may select junk values (e.g., with filterparameters having a value range of 0 . . . 100, setting all values toclose to 0 or close to 100). As another example, in an object detectionor segmentation task, the user may mark areas which do not includerelevant objects (or any objects at all).

Such a failure to provide feedback could be a localized problem with thespecific task, or a repeat problem with some users, e.g., some users are“repeat offenders” which most of the time do not provide useful feedback(due to disinterest, lack of understanding, negligence or any otherreason). As an example, a user may have (for some reason) a strongpreference to “do my own thing.” Such a user may disqualify multiplemodel suggestions B1 to Bn by rating every suggestion as a bad one.Alternatively, such a user may provide an arbitrary low-quality or wrongfeedback (e.g., mark face segmentation cropping rectangles which do notcontain faces at all). The user may then proceed to use a separateversion of the object in question, which may or may not be similar toany of the suggested Bn solutions.

In such cases, system 200 should detect such users, and generally usethe implicit feedback (gathered from their sites) instead of using theirexplicit feedback, possibly ignoring their (incorrect) explicit feedbackaltogether.

The problem may be more substantial in cases in which the output of MLmodel 317 and the desired feedback do not visibly contribute to objectsin the edited website. For example, an embodiment of system 200 mayinclude a face detection model which is used for internal purposes(e.g., as input to other models such as determining regions of interestin images in an image cropping model) but may not directly affectobjects in the edited website. System 200 may still collect feedback forthe face detection model, e.g., by detecting faces in imported imagesand asking the user to correct the detected face rectangles in theseimages. In such cases, users are more likely to provide incorrect orlow-quality feedback without this visibly harming their web site.

The problem may also occur if ML feedback-based proposal module 300 asksfor feedback about data/objects not related to actual user tasks, e.g.,when ML feedback-based proposal module 300 initiates a feedbackinteraction on non-task data as part of an active learning system. Insuch a scenario, ML feedback-based proposal module 300 may be unable togather implicit feedback at all and may only be able to use the(possibly ineffective or otherwise incorrect) explicit feedback. MLfeedback-based proposal module 300 may also handle such cases of missingor faulty feedback by using one or more auxiliary models. For example,one or both of explicit feedback analyzer 325 and per activity AI units310 may use a response evaluator 330 and/or a user evaluator 332,forming part of feedback system 305, as described in more detail hereinbelow. These units may evaluate the quality of feedback responses and ofthe users (respectively). For example, response evaluator 330 mayanalyze the quality and relevance of the responses from a given user.Theoretically, a given user may provide excellent feedback regardingphoto retouching (if he's a professional photographer) but may providelow-quality/incorrect feedback for layout-related issue (if he's a verybad graphical designer).

Response evaluator 330 may compare the feedback of the user withfeedback provided by other, similarly situated users (e.g. determinedthough clustering analysis). If the user feedback is “extremely far fromthe other clusters”, then the user may be defined as being an undesiredoutlier. Response evaluator 330 may also search for whether or not theuser uses extreme values in settings where these values do not makesense (e.g., setting brightness to 100% in photo retouching parameters).Similarly, response evaluator 330 may review output based on the user'ssettings/definitions. If the output is evaluated as very bad using anexternal (supposedly objective) evaluator, such as the layout qualityrater 47 described in U.S. Pat. No. 9,747,258, entitled “System andMethod for the Creation and use of Visually-Diverse High-Quality DynamicLayouts”, granted 29 Aug. 2017, commonly owned by the Applicant andincorporated herein by reference, then the user may be defined as anoutlier.

User evaluator 332 may utilize similar types of analyses, but onprevious answers of the same user, rather than in comparison to a groupor community of users.

It will be appreciated that both evaluators 330 and 332 may take intoaccount the user feedback and its quality (both explicit and implicit).Additional features may be derived through specific analysis and rules,looking for bad responses such as responses using boundary value filtersettings or having user-specified face detection rectangles which do notcontain a face image (as discussed herein above).

It will be further appreciated that both evaluators 330 and 332 may alsotake into account other parameters, such as the history of userresponses, user rating (pro designer vs. regular), user skill level, thecomplexity of the user's sites, user parameters (geography, etc.) orsite parameters. In particular, the evaluators 330 and 332 may evaluatea user response/feedback against the user's choices or final siteversion (the “C” and “D” level versions above) in the previous use ofthe same task (e.g., is the current user feedback consistent with pastfeedback to the same task?).

In one embodiment, both evaluators 330 and 332 may also use humanevaluation (labeling) or some or all of the feedback, e.g., throughinteraction with WBS vendor staff 61 or crowd-sourced evaluators. Suchhuman labeling may be useful, for example, for initial training of theauxiliary models.

It will be appreciated that these auxiliary evaluators 330 and 332 mayalso evaluate the users' skill level as related to the specific task, torelated tasks, to site design in general (e.g., how good are thewebsites created by this designer) or other factors and featuresextracted from the WBS information. As discussed herein above, “quality”and “skill” are not absolute measures, as the “best” results for a giventask may depend on the user community, culture, skill level, etc.

Both evaluators 330 and 332 may also evaluate “soft” parameters of theinteraction. Such parameters may include the time it took the user toanswer, the level of certainty in answer (e.g. did the user select ananswer and then change it before pressing “OK”), and/or biometricparameters of the interaction (such as mouse motions, hand motions, eyemotion, measurable biometric/biological user parameters or otherparameters).

As discussed herein above, explicit feedback analyzer 325 or peractivity AI unit 310 may apply response evaluator 330 to evaluate theuser's feedback (for validity, correctness, etc.) and to determine ifand how the response should be used (e.g. for training), if at all.Response evaluator 330 may use the feedback rating to disqualifyfeedback data whose rating is below a certain threshold. Responseevaluator 330 may also use the rating to assign weights to specifictraining data samples (i.e., user feedbacks) when working withunderlying models which support such weight assignment.

User evaluator 332 may evaluate the users themselves to provide anon-going evaluation if the user is likely to provide useful feedback ornot. Per activity AI unit 310 may activate response evaluator 332 beforeentering the feedback interaction in order to determine which feedbackinteraction to provide, or possibly to not provide a feedbackinteraction or a model solution (e.g., for users who consistently rejectthe solutions offered by a specific model).

It will be appreciated that, since system 200 trains its ML models 317with user feedback, models 317 should generally follow changes in userpreferences and taste. However, system 200 may detect that the userfeedback (both explicit and implicit) may reflect a growing loss ofprecision in the solutions generated by the ML models 317 (requiringusers to implement more significant changes to the suggested solutions Bin order to get their desired result). It may also be detected whenusers consistently reject suggested results, even without editing theseresults.

Such loss of precision could result from a combination of errorsintroduced into ML feedback-based proposal module 300 during design anddevelopment, changes in a specific user group or segment preferences andtastes and general changes in the user's preferences and tastes(including changes which require analysis of features not previouslyprovided to per activity AI unit 310). Such general changes are known asa concept drift.

As discussed herein above, the model results may depend on theclassification of users into groups or segments, and the specificpreferences of each group by community handler 380. However, both thespecific group preferences and the correct assignment of users intogroups may change over time.

The per activity AI units 310 may be able to correct some of the aboveover time. However, some problems (such as implementation bugs) may notself-correct. In particular, the per activity AI units 310 cannot detectand handle cases in which the feature definition they use needs to bechanged (such as whether a specific feature definition should bemodified, or a new feature should be created).

An example of this is a user community that over time gets divided intomultiple subpopulations with very different preferences for some tasks.If a new feature needs to be added that distinguishes between thesemultiple sub-populations, the specific per activity AI unit 310 may notbe able to recommend such a change or define such a new feature byitself.

Vendor feedback handler 390 (FIG. 3) may resolve all of the abovethrough reporting of key metrics and performance from system 200 to thestaff of WBS vendor 61. Such reports may provide the required feedbackto the staff of WBS vendor and may allow them to initiate a human-guidedcorrective action as required. Vendor feedback handler 390 may alsoprovide additional information to WBS vendor 61, such as alerts andstatistics (possibly including user/site/object information related tothe specific issue).

As discussed herein above, ML feedback-based proposal module 300 mayalso use the feedback interaction as part of the tasks connected withediting. It will be appreciated that there are some application areaswhich are centered on the handling of a single object/component (such asan image) only and on additional objects associated with the singleobject (such as cropping rectangle definition or product descriptors forproducts identified in an image).

For some of the tasks, there may be specific per activity AI units 310and implicit feedback gathering techniques, though the feedbackgathering techniques already described above (e.g., based on thepositioning of the generated image in the site) may all be relevant toeach of the application areas as described herein below.

Editing task handler 370 (FIG. 3) may utilize this gathered informationaccordingly to improve editing tasks as supplied by WBS editor 30.Reference is now made to FIG. 7, which illustrates the elements ofediting task handler 370. Editing task handler 370 may comprise an imageresolution improver 371, a face detector 372, a portrait segmentor 373,an object segmentor 374, an image cropper 375, an image enhancer 376, alogo creator 377 and a text generator and editor 378. It will beappreciated that each element of editing task handler 370 may operatewith its own per activity AI unit 310 within ML feedback-based proposalmodule 300 and may utilize both explicit and implicit feedbackinformation.

It will be appreciated that when users upload small images to largecomponents containing them, the images may become pixelated and noisy.To resolve this problem, image resolution improver 371 may employ asuper resolution AI unit 310 to upscale the pixels of images to create asmooth, un-pixelated result, once enlarged. Image improver 371 may alsocombine elements of de-blocking and upscaling.

Image resolution improver 371 may activate the super resolution AI unit310 under a number of conditions, such as on import, whenever thecontainer is resized while editing the page, or when image resolutionimprover 371 detects resolution differences (e.g., when the requiredresolution is significantly larger than the input resolution, e.g. x4larger).

It will be appreciated that image resolution improver 371 may usemultiple feedback interactions such as any of the following actions or acombination thereof. Image resolution improver 317 may display theoriginal image with a set of one or more model-generated images and mayallow the user to select which one to use. These multiple images may begenerated by multiple per activity AI units 310, multiple algorithms,and multiple settings for specific models/algorithms. Image resolutionimprover 371 may also display a model-generated image and may allow theuser to edit it, coordinating with explicit feedback handler 320accordingly.

It will be appreciated that for implicit feedback, image resolutionimprover 371 may use a later version of the image (e.g., from a laterversion of the site or in the published version). System 200 may limitsuch usage to cases in which the user has further modified the imageafter the initial feedback interaction. Image resolution improver 371may also use additional sources of information for the model(s), such asthe image placement, user parameters, and image editing history (asdescribed herein above) coordinating with implicit feedback handler 340accordingly.

Face detector 372 may recognize the appearance of one or more faces in agiven image. Face detector 372 may only perform detection only, or mayalso include face recognition (i.e., determining the identities fordetected face images). In this scenario, a face detection AI unit 310may produce frames (e.g., rectangles) enclosing the detected face(s),but may also produce additional information (such as a more preciseenclosing curve or polygon).

It will be appreciated that the feedback interactions for face detector372 may allow the user to approve, decline, or edit the frames enclosingthe detected faces via explicit feedback handler 320. The editing mayinclude allowing the user to move/resize a frame to correct thedetection, as well as the ability to remove frames or add new frames(i.e., manually identify additional faces).

One problem with face detection AI unit 310 is that the detected facesare not always directly used in the page editing process (thus reducingthe quality of the feedback). As discussed herein above, system 200 mayuse an auxiliary ML model 317 to confirm that the marked areas (e.g., asmodified by the user) do contain faces, so that bad feedback can bedisqualified. Alternatively, or in addition, ML model 317 may utilizeimplicit or explicit feedback gathered in a follow-up operation todetermine if the user clipped or cropped the image according to the facerectangles.

The output of face detector 372 may be typically used as input to imagecropper 375, described in more detail herein below. The output may alsobe used as an indicator for redirection to portrait segmentor 373,discussed in more detail herein below, if face detector 372 detects aface with a probability above a minimum threshold.

Portrait segmentor 373 may remove the background from portrait imagesand may help users create homogeneous images containing multipleportraits. Such images may be needed for web site sections such as “ourteam” which may be part of a manually edited site or of an automaticallygenerated site. In this scenario, a portrait segmentor AI unit 310 maybe trained to distinguish the person from the background, includinghandling of full-body images. The portrait segmentor AI unit 310 mayproduce an enclosing frame (rectangle) or a more precise enclosingcurve/polygon.

It will be appreciated that portrait segmentor 373 may comprise anediting tool which allows the user to define or edit the segmentationboundary. Such a tool may allow the user (for example) to use a brush toclean up areas of the picture so that only what is not brushed is theforeground portrait. The tool may also allow the user to interactdirectly with the segmentation polygon, moving/inserting/deleting nodesof the polygon. It will be appreciated that this tool may providefeedback interaction.

Portrait segmentor 373 may then process the detected portraits and theremaining parts of the image (the “background”) in multiple ways such asby graying out the existing background so to make the portraits standout when viewed, replacing the background (similar to video blue-screen)with a simpler one, such as a uniform color, a simple gradient orpattern, etc., and/or by locating a matching image for use as analternative background. An auxiliary background image selection AI unit310 may be trained to select such images based on the layout of thedetected portraits, the user preferences, and other elements of theuser's site. In particular, the suggested pictures may be selected tomatch the positioning of the portraits in the original image. Portraitsegmentor 373 may offer multiple such potential background images forselection.

Portrait segmentor 373 may also provide tools allowing the user to editthe new combined [image+portraits], or to change the background image orpattern. These may be part of explicit user feedback or follow-upediting during regular site editing (providing additional implicitfeedback). This feedback may be used to train the primary portraitrecognition AI unit 310, as well as an auxiliary background imageselection AI unit 310.

Object segmentor 374 may have similar functionality to portraitsegmentor 373 and may be integrated with an e-commerce system whichcould be a part of the WBS or otherwise operating with the WBS.

Object segmentor 374 may recognize multiple object types (possibly usingmultiple specialized object segmentation AI units 310 for differentobject classes). Such recognition may be made at multiple levels, suchas recognizing the outline of the object, recognizing an object type(e.g., a T-shirt), recognizing the specific object (a T-shirt made byvendor X) or specific attributes of the object (an M-size blue-whiteT-shirt of series Y). Such recognition may also require a catalog ofobjects and attributes for specific object identification and attributedetection.

The detection may be automated, or may be assisted by the user (e.g.,with the user pointing at specific objects to be recognized, or byproviding an approximate outline as a starting point for the detection)and object segmentor 374 may be used to directly populate product pages,including multiple fields in a given product page.

For object segmentor 374, the feedback process may include theinteractions as described for portrait segmentor 373 (such as changingthe segmentation definition), as well as feedback provided by the useridentifying objects or specifying attribute values and tags. Additionalfeedback may be generated by edits made by the user to generated productpages.

Image cropper 375 may provide the automatic cropping of images,selecting the “important” or “interesting” part of the image for use inan edited page. It will be appreciated that these concepts(important/interesting) may be subjectively defined, and image cropper375 may implement user-specific, image cropping AI units 310 in additionto a general universal image cropping AI unit 310. Image cropper 375 mayalso utilize image cropping AI units 310 which are specific toindustries or other sub-groups. For example, for urban street images,the sub-area importance rating for a fashion design web site may becompletely different from that used for traffic planning web site.

Image cropper 375 may create a “heat map” of the image and may suggestthe best cropping rectangle(s) for it based on the main focal points andobjects in the picture. The image cropper AI unit 310 may use inputsfrom other AI units 310 as described herein above, for face detection,portrait segmentation, and object segmentation (including identificationof object types and selection of objects which are more relevant orimportant). The image cropper AI unit 310 may display the inputs fromthese supporting AI units 310 (e.g., displaying rectangles enclosingrectangles around detected faces) as part of the image cropping preview(described in more detail herein below) in order to help the user in hisdecisions and feedback.

The image cropper AI unit 310 (and some of the underlying supportingmodels) may also take into account the expected use for the croppedimage (in a manually edited site or an automatically generated site).This could be, for example, information related to the design and layoutof the target page or information related to the e-commerce product pagecontaining the final image.

The image cropper AI unit 310 may generate multiple possible regions.This may enable the user to select the best region to use or to supportsystems and scenarios which support multiple cropped sub-images (e.g.,by creating a multi-image gallery component from the multiple croppedsub-images). Even in this latter scenario, the user may still want toselect a subset of the multiple proposed regions to use. Multi-regionsolutions may also be relevant in the context of site generation systemswhich can use multiple (and possible interrelated) parts of the image ina different position in a suggested layout.

Image cropper 375 may display a preview of the cropping options incontext, e.g., inside a specific product page which contains the croppedimage and may allow feedback interaction (and editing) to occur withinthis preview.

For feedback interaction, image cropper 375 may display a preview of theimage together with the suggested cropping rectangle(s). The display maybe stand-alone or made in context, e.g., inside the e-commerce productpage which will contain the cropped image (showing multiple versions ofthe page) and may allow feedback interaction (and editing) to occurwithin this preview. Image cropper 375 may then allow the user to moveor resize the suggested frames, select the specific frame(s), add framesor remove frames.

Image cropper 375 may later collect follow-up (i.e. implicit) feedbackbased on the actual use of the images and on any changes made to itszooming and cropping when the user edited the page.

Image cropper 375 may also collect feedback from users who manuallyplace WBS elements (e.g., text or small images) overlapping areas of animage used as a background. In this scenario, image cropper 375 mayinfer that the overlapped areas are the less important ones and may usethis information to train its image cropping model 317. Such feedbackcould be collected from images cropped by image cropper 375, but alsofrom images cropped manually outside of system 200 (or not cropped atall). Such feedback could serve as a source for large-scale initialsupervised learning for the image cropper AI unit 310 even before anautomatic cropping facility is made available to users.

It will be appreciated that multiple users may handle the same image (orvery similar images) differently, e.g., selecting different areas of theimage on which to place other things. This could be arbitrary to someextent (e.g., due to different users having different priorities ortaste) or related to various parameters or the users or work scenario.Image cropper 375 may provide specific parameters or the user/scenarioas additional features for the training of the learning algorithm.

It will be further appreciated that the image cropping AI unit 310 mayalso be used outside the realm of regular image cropping. For example,site generation system 40 (as described herein above) may utilize theimage cropping AI unit 310 when selecting which layout elements shouldoverlap an image. Thus, image cropping AI unit 310 may perform initialanalysis to determine the important and non-important areas of abackground image. The results of this analysis may be provided to sitegeneration system 40, and used to determine which layout elements to usewhen generating a site, and where to place them (e.g., so they overlapthe less important part of the background images). Alternatively, animage cropping AI unit 310 model may be integrated with a ML model orrule-based engine used by site generation system 40 (so the croppinganalysis and the site generation are performed together).

Image enhancer 376 may give users the option to improve their images by(as an example) automatically changing the brightness, contrast,saturation and other properties of their images. Generally, the userinterface of WBS editor 30 may allow the user to specify values forbrightness, contrast, saturation, sharpness, etc. via multiple levers,rotating buttons, etc. Image enhancer 376 may offer an “auto” option(for each setting, a combination of settings or all setting together)and may allow the user to correct the generated/modified settings,either during the initial import of the image or during regular page orimage editing (e.g., as a pop-up user interface (UI)).

It will be appreciated that the user feedback gathered for imageenhancer 376 may be based on the changes the user makes to the varioussettings. Image enhancer 376 may train (for example) an imageenhancement AI unit 310 based on user changes at any time, such as whenusing auto settings or when using manual settings. Image enhancer 376may also omit boundary values of user settings from the training datawhen these values are plainly wrong (such as 100% brightness), or asdetermined by user evaluation model 332.

It will be appreciated that for image enhancer 376, interactiongenerator 315 may provide suggested settings for the various scenarios.Alternatively, it may modify the underlying image directly.

Logo creator 377 may provide automatically generated logo suggestions tosite creators. Logo creator 377 may utilize a logo creation AI unit 310to create logos based on information (text, images, etc.) known aboutthe user, e.g., from information gathered by site generation system 40and based on user responses, analysis and gathered information frominternal and external sources. This information could include, forexample, the business name, slogan, picture associated with thebusiness, an existing off-line logo or a logo used in an existingnon-WBS site. The logo suggestion may thus include text (with specificfont, size, and other parameters), pictures, graphics art, etc. Thegenerated logo may combine multiple such elements.

Logo creator 377 may typically generate multiple suggestions and mayallow the user to rate them. This could be done, for example, by ratingeach suggestion separately or (preferably) rating pairs of suggestionsone against the other (i.e., which one in the pair looks better). Theseratings and the final choice made by the user may then be used to trainthe logo creation per activity AI unit 310.

It will be appreciated that there may be differences between the scoresprovided by designers and those provided by users. Thus, the data andtraining provided by actual users may be more representative thaninitial training by professional designers.

Text generator and editor 378 may comprise a text generation AI unit 310which may provide a structured generation and editing environment formedia and text elements embedded in components in the site. An exampleis a video editing environment which provides a set of recommended videosegments, along with a tool to modify the segments, embed information inthe segments, concatenate the segments, add video effects andtransitions etc. A further example may be an audio editing environment,offering similar capabilities when handling audio segments.

A further example may be a structured text editing environment similarto the one described in US Patent Publication No. 2019/0163728 entitled“System And Method For The Generation and Editing of Text Content inWebsite Building Systems”, published 30 May 2019, commonly owned by theApplicant and incorporated herein by reference. Such an environment mayprovide a set of text elements or “mini templates” which can “worktogether” in order to form text elements. These can be used to edit sitetext sections such as “about us”, “our products”, “terms of use”, teambiographies, specific product descriptions, etc. The “mini templates”may contain placeholders which may be filled using the informationavailable to the system (such as the site's business name, address orproduct list, or other details available to the site generation system40 or in an underlying database).

Text generator and editor 378 may also comprise a specialized editor toprovide image creation based on a set of suggested picture or videoelements (e.g. cartoon character images and backgrounds) andmanipulation operations. The modification and embedding operations maybe provided at a number of levels such as: simple—add voice over orcaptions to video segments, intermediate—modify text, audio andsub-images viewed inside a video segment (as is sometimes done whentranslating a video from one language to another) and complex—actuallyspecifying elements of movement and behavior for characters and objectsin a video segment, and creating a parameterized video segment.

Text generator and editor 378 may gather feedback from multiple users'activity in creating and editing these segments. The gathered feedback(and training data) may further include user features. For example,users in one region may have different preferences compared to users inother regions.

Based on the gathered feedback, text generator and editor 378 (togetherwith its text generator and editor AI unit 310) may then determine whatmaterial and options to offer the user (e.g., which text templates orsegments, which video sub-segments, which operations to suggest, etc.)and may filter and/or rank the possible options. It will be appreciatedthat generally for implicit feedback, system 200 may use feedbackinformation gathered by a follow-up activity AI unit 310, such as theimage cropping or portrait segmentation models as discussed hereinabove.

As discussed herein above, editing task handler 370 and its elements mayprovide support to tasks handling a single object. This functionalitymay be used to support processes which may have been previouslyperformed using specialized rule engines employing predefined sets ofrules generated from a study of the problem domain. These rules may bedifficult to construct and even more difficult to maintain. It will befurther appreciated that system 200 may use its machine learning basedsolutions instead of rule engines or may combine the two technologies,integrating output from both sources. For example, an AI-based methodmay detect groups of web-elements and a rule-based method may thendetermine how to lay them in a mobile view based on their variousparameters.

Furthermore, system 200 may use an existing rule-based system to provideinitial supervised training to an underlying per activity AI unit 310.Such initial training could be performed by using existing outputs ofthe rule engine (e.g., existing site analysis and site transformationresults), as well as using the rule engine on gathered sites (e.g.,other unprocessed WBS/non-WBS sites as well as randomly generated ones).

Editing task handler 370 may also provide the user with specific editingor mark-up tools which may allow the user to review or edit the sourcematerial to be processed (analyzed or transformed). Such tools may allowthe user to provide additional hints or associated information which mayprovide additional inputs to the rule-based system or the ML models 317.

It will be appreciated that editing task handler 370 may operate MLfeedback-based proposal module 300 to improve analysis of the existingWBS information, typically focusing on the layout and other informationon the currently edited page. Such analyses may result in the creationand modification of an auxiliary structure (such as definitions of agrouping of components or a higher-level layout description) but do nottypically affect the directly visible site page.

The different analyses may use multiple features extracted from thelayout and the components, including features reflecting the results ofsemantic analyses or other analysis types (e.g., as per the types ofanalysis detailed in U.S. Pat. No. 10,176,154 entitled “System andMethod for Automated Conversion of Interactive Sites and Applications toSupport Mobile and Other Display Environments” granted 8 Jan. 2019 andin US Patent Publication No. 2018/0032626 entitled “System and Methodfor Implementing Containers Which Extract and Apply Semantic PageKnowledge”, published 1 Feb. 2018, both of which are commonly owned bythe Applicant and incorporated herein by reference.

Reference is now made to FIG. 8 which illustrates the elements of sitefunction updater 400 which may provide support to tasks handlingmultiple WBS components together. Site function updater 400 may comprisea component grouper 401, a component group labeler 402, an objectanalyzer 403, a component orderer 404, an object transformer 405, adesktop to mobile transformer 406, a WBS importer 407, a responsiveediting supporter 408, a template replacer 409, a manual site adapter410, and a design suggester 411.

It will be appreciated that, like editing task handler 370, the elementsof site function updater 400 may each operate with their associated peractivity AI unit 310 of ML feedback-based proposal module 300. Each peractivity AI unit 310 may be universal, per a predetermined community orper user. The elements of site function updater 400 may use ML models317 adapted to natural language understanding, with the WBS componentsas the symbols fed into the models. Such symbols may be directlygenerated by the WBS or may be the result of an underlying analysis toidentify the layout and the components in a non-WBS web site (e.g., whenanalyzing non-WBS sites which are to be imported into the WBS, or areotherwise to be analyzed for extracted information). Suchlanguage-related models may include, for example, RNN (Recurrent NeuralNetwork) and similar machine learning models.

It will also be appreciated that language-related models typicallyhandle information as a series of input symbols, which is inherently onedimensional. On the other hand, the typical web page structure istwo-dimensional and can be considered three dimensional when taking thedisplay order z coordinates into account. The web page structure may beeven more complex, particularly if the container hierarchy needs to beanalyzed.

Elements of site function updater 400 may use algorithms to determinethe “natural order” of the elements in the layout, thereby convertingthe 3D+ layout data into a 1D symbols series. Such an algorithm may besimilar to that described in the definitions of the super-node creatorand orderer in U.S. Pat. No. 10,176,154.

It will be appreciated that ML feedback-based proposal module 300 mayinclude attributes of the original component as features of thegenerated sequential symbols handled by per activity AI unit 310. Suchattributes may include geometrical attributes (such as the original X/Ycoordinates, the height, and width of the components and its z-order) aswell as other attributes of the components.

It will also be appreciated that each element of site function updater400 may allow the user to provide explicit or implicit feedback (asdescribed herein above). For example, in a component grouping analysistask, the users may provide initial labeling of groups, and may furtherprovide labeling of the generated analysis (such as component grouping)as to which is wrong or non-optimal.

It will also be appreciated that the analysis tasks provided by sitefunction updater 400 may be standalone tasks but may also be the firststage in a transformation task as described in more detail herein below.

One such task is component grouping. Similar to the system described inUS Patent Publication No. 2018/0032626, component grouper 401 maysupport persistent grouping of objects and may offer multiplecapabilities based on such groupings, such as specialized behavior forediting operations (e.g., resizing and sub-element insertion, deletion,or editing) or specialized connections to underlying data repositories.

Component grouper 401 may utilize a component grouper AI unit 310 todetect such groups and understand their layout and may utilize a MLmodel 317 similar to that shown in FIG. 6. The result of such detectionis a definition of the component grouping which may be “flat” (i.e.,consist of a single set of group definitions) or hierarchical (i.e.consist of a hierarchy of groups and super groups).

Reference is now made to FIGS. 9A, 9B and 9C, which illustratealternative methods for component grouper 401 using ML models based oncomputer vision, based on triplet analysis, and based on a combinationof triplet analysis and computer vision, respectively. Each process maystart with extracting (step 210) web elements, as in the method of FIG.6.

For computer vision model, the method may continue in FIG. 9A withgenerating (step 220) an image of the page containing the extracted webelements and may provide the resultant site image to a trained objectdetection neural network. For the training, each page may be rendered asan image, whereby each element type is given a color, and bounding boxesare manually drawn around each element. The model learns (step 222) todraw bounding boxes around groups of elements. This is analogous to theway an image object detector is trained to draw bounding boxes aroundobjects in an image. These bounding boxes indicate the groups ofelements and hence no further clustering is required. These images arethen used to train the neural network via supervised learning (boundingbox regression). The trained neural network may then produce (step 222)a set of bounding boxes (for potential groups) around the elements ofthe page which may then be processed (step 224) into component groups,as described in more detail herein below. It will be appreciated thatthe training input may comprise site images and their associated groupdefinitions. The model in FIG. 9B is termed the Triplet Model andinvolves training a model to classify groups of three elements into oneof three categories: 1) All elements are in the same group, 2) allelements are in different groups or 3) two of the three elements are inthe same group (step 234). The model uses relational geometric features(for example distance, overlap, intersection, union) between theelements in order to classify the triplets. These features are extractedin step 232. Once all the triplets are classified, the groups arereconstructed in postprocessing by clustering elements which are mostoften classified as belonging together, using DB Scan clustering (step236).

The computer vision and triplet methods may be combined, as shown inFIG. 9C. Initially, the computer vision operation of FIG. 9A may beutilized and its groups may be provided as input to the triplet methodof FIG. 9B. This may provide more accurate grouping of elements. Thecombination of the two methods enables the computer vision model toidentify high level groups and implements the triplet model on eachhigh-level group in order to find better lower level groups within thehigh-level groups.

FIG. 9C may be implemented on a specific container and recursively foreach component node within the containment hierarchy, usually startingwith the page as the top node. For each node (e.g. a container which hasX sub-elements inside it), the step 230 creates each possible tripletfrom these X elements

$( {{i.e.\mspace{11mu} \begin{pmatrix}X \\3\end{pmatrix}} = ( {{X!}/( {{( {X - 3} )!}*{3\ !}} )} } $

triplets would be created). These are analyzed as above to figure outthe best grouping. Finally, the information is merged “flowing up thetree”.

Reference is now made to FIG. 10 which illustrates a hierarchicalgrouping process. As is illustrated, page A shows pairs of pictures andassociated captions (such as a person's picture and name) which shouldbe grouped together ([b,e], [c,f] and [d,g]). Using its associatedcomponent grouping AI unit 310, component grouper 401 (FIG. 8) may groupthe components into three groups (k, l, and m) as shown on page B.Furthermore, component grouper 401 may group the three pairs together(on page A) to form an “our team” type group (j) (on page B) as definedby the shown hierarchy in FIG. 11 for pages A and B to which referenceis now made.

It will be appreciated that the grouping hierarchy may differ from oreven contradict the “natural” page containment hierarchy. For example,the grouping hierarchy may include sub-trees consisting of elementswhich technically reside in the same container level (i.e., are allsiblings), and thus have no previously defined hierarchical relationshipbetween them.

Furthermore, group definitions may cross container boundaries. Forexample, the page designer may have originally placed the three pictures(b, c, and d) from FIG. 9 in one container and the three text captions(e, f, and g) in a second separate container to help with their editingand alignment. However, the (content-related) grouping may cross thesecontainer boundaries as described herein above.

Component grouper AI unit 310 may be trained to use the containerhierarchy information (if available as a feature) and also to “break”the container hierarchy where appropriate (e.g., based on humanlabeling).

Component grouper 401 may also integrate predefined grouping rules andmethodologies (e.g., similar to the super node creator described in U.S.Pat. No. 10,176,154). Component grouper 401 may apply such rules andmethodologies in conjunction with its component grouping AI unit 310 orto provide component grouping AI unit 310 with initial training data.

Component grouper 401 may also train its component grouping AI unit 310to provide layout/group understanding, e.g. it may assign specific typesto groups at all levels. Such types could be defined as being in the“WBS component world” (such as a list, a matrix, an image+caption, animage gallery, etc.) or the “real world” (such as a person's details,team members, offered products, etc.). System 200 may provide relevanttaxonomies of both types, possibly including hierarchies of types. Suchtraining may also be provided by a labeling tool which allows the userto label groups with their type. This group understanding serves as anadditional layer of information which should be determined in additionto the group detection as described herein above.

Component grouper 401 may thus gather explicit feedback by offering theuser a suggested grouping definition (possibly including a suggestedgroup type as noted above) and allowing the user to, for example,approve the group definition, to reject the group definition, to selectfrom amongst multiple grouping suggestions and to edit the provideddefinition, proposing the user's grouping definitions or alterations(e.g., merging, splitting, re-ordering or re-parenting groups).

Component grouper 401 may gather follow-up implicit feedback from lateroperations performed during editing. In particular, WBS editor 30 mayoffer group and ungroup operations, and these may provide additionalfeedback. Component grouper 401 may also gather feedback based onanalysis of other operations, e.g., component insertions/deletions andoperations performed on sets of elements. For example, if a text fieldand an image field are typically changed together, this may indicatethat the two are interconnected (e.g., the text field describes thecontent of the image field) and should possibly be grouped.

Component group labeler 402 may support users when labeling groups ofcomponents in a (possibly hierarchical) manner. Such labeling mayinclude both the composition of the group (i.e., what components thegroup includes) and the group meaning (i.e., what is the function of thegroup, such as “team member”, “picture+caption”, etc.). These labels maythen be used to train other ML models 317 for group identification andgroup understanding.

Reference is now made to FIGS. 12A, 12B, and 12C which illustrate grouplabels for the same web page defined at three levels of hierarchy. FIG.12A shows a high level of hierarchy for the components. As a result, thecomponents of the page have very general component labels, such astitle, headline and journal articles. FIG. 12B shows a more detailedhierarchy of the components, where columns are added to the previous setof component types, along with their associated labels. FIG. 12C shows ahighly detailed hierarchy of the components, where text and image areadded to the previous set of component types, along with theirassociated labels.

Component group labeler 402 (FIG. 8) may label groups at various levelssuch as page sections, entire page, page sets or entire sites. Componentgroup labeler 402 may be used to label sites developed within the WBS orfrom external sites (developed using other WBS's or non-WBS technologiesor platforms). Component group labeler 402 may consist of multiple toolvariants or embodiments aimed at performing the labeling for multiplesupported platforms.

For sites created or hosted by the WBS, component group labeler 402 maybe integrated with the WBS runtime server 20. Thus, it may support thelabeling process with full information about the displayed components,their layout, their state, their content, and any additional informationavailable to the WBS (including historical information such as editinghistory).

Component group labeler 402 may also be integrated with the user'sactivities in the site and with site changes occurring during thesession and may thus “follow” the user and the site, synchronizing thelabeling work with the site changes. Component group labeler 402 mayfollow for example, user activities which cause part(s) of the page tochange, so previously labeled areas may become hidden or invisible, andnew page areas, components or component versions or configurations maybecome visible. Component group labeler 402 may modify the groupingdefinition as a result. Component group labeler 402 may also followchanges to the site occurring due to dynamic layout, e.g., due to useredits (e.g., causing components to change their size, move or affectother component's layout) or due to non-user changes (components whichreflect changes by made other users or dynamically changing dataembedded into the components). It may also follow changes to the siteoccurring due to response layout, e.g., when the site switches betweenmultiple layout versions depending on the width (or other parameters) ofthe display area.

Component group labeler 402 may also affect the WBS's functioning andbehavior. For example, the component group labeler 402 may instruct WBSruntime server 20 to display the components handled by the user in amodified, alternative, simplified, frame-only or another mode whichmakes it easier to label them (e.g., by removing visual elements orclutter or otherwise clarifying the relationships between the variouscomponents).

Component group labeler 402 may also use the available information (fromthe WBS or otherwise) in other ways to support or to provide possiblehints and directions to the user such as adding specific markings,alteration, animations or other UI elements (such as handles orinter-element connections) to components which may form a group.

As another example, component group labeler 402 may show the user theorder in which components were added (or edited), or otherwise markcomponents which were added or edited during a given period or session.Such information may be directly available to the tool (based on theediting history information) or may be generated based on data analysis.

For sites implemented on external WBS platforms or non-WBS sites,component group labeler 402 may be implemented (for example) as abrowser add-on or extension. Component group labeler 402 may then act asa layer of additional UI displayed in conjunction with the underlyingsite display and may allow the user to label the groups (and grouphierarchy) in the displayed page(s) as is illustrated in FIGS. 13A and13B to which reference is now briefly made. FIG. 13A illustrates alabeling UI and FIG. 13B shows the labeling UI once a few components areselected in order to be grouped.

In this scenario, component group labeler 402 may further interact withthe browser and the displayed page document object (DOM) structure,e.g., by analyzing the DOM (on page loading, in real-time or otherwise).This interaction may allow component group labeler 402 to synchronizeany group marking activity with changes in the displayed page.

Component group labeler 402 may further interact with the DOM of theunderlying displayed page by making modifications to the DOM, in orderprovide hints to the user performing the grouping (similar to thechanges performed to the page UI through interaction with the WBS asdescribed above). Alternatively, component group labeler 402 may provideadditional UI displays and hints via other means, such as via anadditional (possibly semi-transparent) layer displayed on top of thedisplayed page.

Thus, component group labeler 402 may help in marking both WBS andnon-WBS sites. It may (in both cases) use additional informationavailable to it (e.g. as result of its analyses, extraction from the WBSor the DOM structure, etc.) as additional features added to the labelsprovided by the user.

In a typical scenario, component group labeler 402 may be usedinternally by WBS vendor 61 to train per activity AI units 310 which canbe later deployed to regular (external) users. However, the WBS may alsodeploy system 200 to external users in some scenarios in which suchusers may be requested to provide grouping feedback for specific sites.For example, when a user wishes to create a site in the WBS using sitegeneration system 40 and the user provides details of additionalrelevant sites (e.g., the user's own or competitor sites), theinformation extracted from such sites can be used by site generationsystem 40 when generating or modifying a site for the user. As part ofthe interaction, the user may be asked to point out or mark elements ofthe provided site. Such marking may include grouping information and mayprovide information about them, which site generation system 40 canprovide to component group labeler 402 for use as part of the grouplabeling process.

Component group labeler 402 may also implement loopback integration withgroup labeler AI unit 310, using the grouping data. This way, componentgroup labeler 402 may submit the page(s) being reviewed (or sectionsthereof) to group labeler AI unit 310 and may receive an initialgrouping suggestion(s) from interaction generator 315. Component grouplabeler 402 may then present these suggestions to the user (inconjunction with the displayed page). The user may then review thesesuggested grouping indications (including editing them or creating adifferent grouping), and component group labeler 402 may provide thisgrouping to group labeler AI unit 310 for training.

Component group labeler 402 may compare the work done by multiple people(labelers) for the same pages(s) or component sets to see if they agreeon how to define grouping for a given page or component set. Such acomparison may be based, for example, on the value of an adjusted randindex. This index measures the degree of similarity between differentgrouping definitions. Presumably, if multiple persons recommend the same(or similar) labeling, this labeling is more accurate or otherwise morereflective of what the best grouping should be.

Object analyzer 403 may detect specific objects or patterns in a site,such as logos. This is particularly relevant when analyzing a non-WBSsite to detect a logo (e.g., for use in the site generation process).Such an analysis task can also include detecting “user composed” logosin existing WBS sites which were not marked or indicated as a logo.

Object analyzer 403 aims to detect sets of components which togetherform a logo. It will be appreciated that a user may compose a logo bycombining multiple text, image and graphic elements (e.g., a starshape), without any indication that these elements form a logo (or anytype of group together). Detecting such a logo could be especiallydifficult when analyzing a non-WBS site, in which the components are notdirectly defined as such, and their definition should be detected in theunderlying HTML code. Object analyzer 403 may also include detecting asingle component which is (by itself) the analyzed site's logo.

Object analyzer 403 may gather feedback on the logo detection taskthrough an approval/rejection interaction with the user when presentinga detected logo. Object analyzer 403 may collect follow-up feedback bydetecting if the logo was used in the published version of a sitecreated based on the analyzed site (by direct conversion or sitegeneration) or by detecting if the logo was moved to the top area for amobile version of the site or added to a header which appears inmultiple pages of the site. It will be appreciated that such operationsmay provide further feedback on how acceptable (or useful) the detectedlogo is to the user.

As discussed herein above, system 200 may use an ordering algorithm(such as the one detailed in U.S. Pat. No. 10,176,154) so as to define alinear order between displayed components which are arranged in a3-dimensional layout. The linear order is typically defined based on anatural reading order of the page, although such an order may bedifficult to define or may be ambiguous, and other possible orders mayexist. Such a linear order may be required, for example, to utilizelanguage-related per activity AI units 310 on component sets or toutilize AI units 310 which use a symbol sequence-based ML model 317.Such ordering may also provide the basis for additional systemcapabilities (and may provide input to additional task-specific AI units310) such as conversion of an existing layout to mobile or differentdisplay parameters, providing responsive editing and displaycapabilities and providing alternative views of the site page(s) whichrely on a given component order, such as reading the site for a blinduser as part of the system's accessibility support.

Component orderer 404 may utilize an ordering AI unit 310 for componentordering which may be initialized using an explicit component orderinganalysis (as discussed herein above) and may be further trained throughfeedback from users (possibly including feedback from end-users of theweb sites created by WBS users or designers). Component orderer 404 maygather feedback by providing a UI which may allow attaching ordernumbers to components. Alternatively, component orderer 404 may have asimple “Oops” button which can be used by the end-user whenever themodel provides the wrong order. The Oops button may be combined with acursor to point to the location of the error (e.g., the location of thewrongly place component). Such a button may be used, for example, by ablind user to cause system 200 to re-read a mis-ordered set ofcomponents using a different order.

Object transformer 405 may transform a collection of objects, such asthe components in a web page with their layout and other associatedinformation (the source object set) into a modified target object set.The transformation is typically based on a preliminary analysis of thesource object set, such as the grouping and ordering analyses describedherein above.

It will be appreciated that the following discussion on objecttransformer 405 is directed to web page transformation. However, thesame processes may apply to page sections or a subset of a page (e.g.,the content of a given container), page sets (including complete websites), arbitrary component collections as well as other non-webcomponent or element sets.

It will be appreciated that a transformation can be made at multiplelevels such as when both the source and target object sets are the samewebsite page (e.g., in the WBS) and transformation involves changingsome components, layout, and attributes. An example would be modifyingthe components and the layouts in a desktop version of the page togenerate a mobile version of the page.

Another level is when the source and targets object sets are generallydifferent pages in the same platform (e.g. WBS), and object transformer405 may transfer content, layout and design information from the sourceobject set to the target object set. An example would be processing asite built based on a specific template and transferring its contents,layout, and design (and the changes made to the original sourcetemplate) to a new target template.

A further level is when the source and target object sets are pages ondifferent platforms. An example would involve importing pages to the WBSfrom a different WBS, or a non-WBS site. Unlike the previously describedtypes of transformation tasks, in this type of transformation, thesource and target object sets are really in “different languages”, theset of available components and attributes may be substantiallydifferent between the source and the target platforms. In fact, whenconverting from regular (HTML/JS) web pages, the source page may have nocomponent definitions (unlike pages defined in the WBS of the presentinvention).

Object transformer 405 may use an object transformer AI unit 310 adaptedto the realm of natural language processing including both languageanalysis and generation models (possibly through ordering of the variouscomponents). Some of these tasks (and the related facilities and models)are described in more detail herein below.

The object transformer AI units 310 may be trained using matching pairsof source and target object sets. Such pairs can be provided by WBSvendor staff 61 and others as discussed herein above. Such pairs mayalso be gathered using implicit feedback data extracted from userscorrecting an initial solution provided by system 200. The per objecttransformer AI units 310 may also be trained using other explicit andimplicit feedback forms and interactions as discussed in more detailherein below.

It will also be appreciated that existing WBSs often need to retargetsites built for the desktop to different screen sizes, such as thosefound in tablet and mobile devices (as well as different display windowssizes). Such tablet and mobile devices have no standard screen size, andnumerous sizes exist in the market. The transformation made to the pagesto conform to different screen sizes is substantial and may involvere-arrangement of the components so as to avoid horizontal scrolling andminimize vertical scrolling. It may also involve changing componenttypes (e.g., changing a grid gallery to a mobile-ready slider gallery),modifying the component's layout and other attributes, adding components(e.g., a mobile navigation bar), dropping components (e.g., decorativecomponents which are not needed in the mobile version) and otherchanges.

Existing systems are typically based on analysis of the page layout andelements, and the use of rule engines to provide layout changes andother page changes. One such system is described in U.S. Pat. No.10,176,154.

Desktop to mobile transformer 406 may use a desktop to mobiletransformer AI unit 310 that can be trained using the relevant ruleengine on existing sites. It may also be trained using previouslyconverted sites (which may have been edited by their designer to improvethe suggested rule-based version). It may also be trained based on newlyconverted sites, i.e., based on the changes made by the users to sitesconverted using the model.

An alternative training approach may use an analysis-based pagetransformation algorithm assisted by an appropriate per activity AI unit310. For example, U.S. Pat. No. 10,176,154 describes such ananalysis-based algorithm which incorporates grouping of relatedcomponents (e.g., a picture and caption pair) and hierarchicaldetermination of a reading order of the page's components. Theapplication describes multiple methods for the analysis of the page anddetermination of such grouping and ordering.

WBS importer 407 may analyze non-WBS sites and may create a matching WBSsite. This could be conversions of sites from a source WBS to a targetWBS or could be the conversion of a regular (HTML-based) site to a sitewithin a target WBS. In the first instance (WBS to WBS), the main issueis mapping components and attributes from the source WBS to the targetWBS. In particular, the two WBSs may be using different data models,architectures, and/or concept sets. For example, one of them may allowhierarchies of pages to be created, while the other may use a differentmechanism to connect related pages. One of the WBSs may define somecapabilities (such page routing menus) using components and layoutdefinitions whereas the other WBS may express these capabilities viadata tables or procedural definitions.

In the second case (HTML to WBS), the WBS importer AI unit 310 has todetect complex components from within the HTML, as the HTMLrepresentation of such complex components may consist of multiple HTMLelements which may seem unrelated. For example, a single WBS videoplayer component may be implemented in HTML using multiple frames, avideo display window, multiple buttons (for stop, start, pause, . . . )and other controls. The video-player elements may be spread overmultiple HTML element sub-hierarchies and mixed with other pageelements. The model should be able to recognize such combinations ofHTML elements and to map them into a single video player component (withthe right attributes and layout information).

WBS importer 407 may use multiple WBS importer AI units 310. Inparticular, an initial WBS importer AI unit 310 may provide an analysisof the element hierarchy in the site. The WBS importer AI unit 310 mayalso analyze the context of the element in the hierarchy, in order torecognize (for example) that a given sub-hierarchy represents an “aboutus” element of the site. Such analysis may be expressed using semanticssimilar to that for layout elements as described in U.S. Pat. No.10,073,923.

A second WBS importer AI unit 310 may handle the analysis of the layoutof the site, as well as the understanding of additional properties ofthe site such as the responsive behavior of the site as described inmore detail herein below (i.e. what are the breakpoints in screen widthin which the site changes its element configuration to provideresponsive design). An exemplary responsive design and editing system isdescribed in US patent application (Attorney docket number P-25078-US)entitled “System and Method Providing Responsive Editing and Viewing,Integrating Hierarchical Fluid Components And Dynamic Layout”, filed onthe same day herewith, commonly owned by the Applicant and incorporatedherein by reference.

Reference is now made to FIG. 14 which shows how a given layout can bedisplayed on multiple available screen widths. As is shown, a givenlayout S1, consisting of 5 components a-e, is displayed on fourdifferent screens having widths (W1 . . . W4). For widths W1 and W2,components a-e are laid out in a similar manner, with component a ontop, components b-d in a single row under component a, and component eon the bottom. For much narrower width W3, component a remains on top,followed by component b, followed by components c and d in a single row,and component e on the bottom. For very narrow width W4, only componentsa-d are shown, in a single column.

In FIG. 14, the widths W1-W4 are divided into ranges by two breakpointsbp1 and bp2. As can be seen, changes in the width may cause thecomponents a-e to change size, position, relative relationship and evento be removed from the layout in some cases (as is component e in thewidth w4). WBS importer 407 may analyze the site to determine if suchbreakpoints are already defined for the site, and which breakpointvalues should be used (pre-defined ones or new ones). WBS importer 407may also analyze any existing responsive behavior (which may be mappedto the WBS version) and may determine the best responsive behavior touse.

It will be appreciated that responsive sites can be viewed as siteshaving a set of breakpoint ranges with a separate layout and componentconfiguration for each of the ranges. Furthermore, each range may haveits own dynamic layout rules which may control the behavior of thecomponents (e.g., moving or resizing) when the display size changes withthe range (or other changes occur such as component content changes).

WBS importer 407 may provide as input to its per activity AI unit 310 anoff-line copy of the original site, using a single form of the site(e.g., desktop only). The second per activity AI unit 310 may requiresome interaction with the actual site, e.g., in order to test it undermultiple display configurations (which may also be done with the actualsite or an off-line copy thereof).

It will be appreciated that for WBS importer 407, the feedback may beprovided as described herein on above (training on converted sites,implicit user feed resulting from model result correction, etc.). System200 may also provide a specialized imported site editor to provideimplicit feedback just after the import processing is ended. Such aspecialized editor may provide additional feedback, e.g., displaying thehierarchy analysis results and allowing the user to edit the hierarchyin ways not provided by WBS editor 30.

As discussed herein above, responsive site structure is described in thecontext of importing sites into the WBS. In this context, system 200 mayimport existing responsive site definitions from non-WBS sites.

However, responsive site handling may also be relevant when directlyediting sites defined within the WBS. This may include adding responsivebehavior (including breakpoints and multiple configurations) to apreviously non-responsive site, as well as editing existing responsivesites. It will be appreciated that editing a responsive site may includeediting the breakpoint range definitions (modifying, adding, removing,splitting, or merging). Such editing may also include editing thecomponents and their layout or configuration for a specific breakpointrange, affecting changes which system 200 may try to apply (with therelevant modifications) to the layout or configuration for otherbreakpoint ranges.

As an example of the above, the user may make a change (such as adding anew component) to the layout for a specific breakpoint range. System 200may apply the changes (if and when relevant and with the relevantadaptation) to the layout for the associated other breakpoint ranges.Assuming (for example) that a given WBS site has three configurations(for three breakpoint ranges), which may be two desktop configurations(A and B) and one mobile configuration (M). If a component is added toconfiguration A, system 200 may support automatically adding thecomponent (with modified size and position) to configuration B, andadding a mobile version of the component to configuration M.

Responsive editing supporter 408 may use changes and edits made by theuser to one website configuration or another to train its associatedresponsive editing AI unit 310. In an alternative embodiment,interaction generator 315 may generate multiple alternatives forapplying the user's changes made to one configuration on the otherconfigurations. The user may be asked to select which alternative touse. Responsive editing supporter 408 may use this feedback to train itsresponsive editing AI unit 310. It will be appreciated that responsiveediting supporter 408 may operate either with site generation system 40or with editor 30 or both, as necessary.

As discussed herein above, system 200 may provide the user with theability to perform template replacement. Users (designers) often buildtheir sites based on a template provided by WBS vendor 61. The user mayperform a substantial modification to this underlying template,including design and layout changes, insertion and removal of pages,page elements and site sections as well as the entry of user-specificsite text, media, and data. The user may later desire to switch to adifferent template, which would require the user to map the changes(layout and content) to the new template and reapply them as appropriate(in a template conversion session).

Template replacement may or may not be possible in some cases. Forexample, sites created using site generation system 40 are highlystructured and are typically based on a hierarchy of layout elements (asdescribed in U.S. Pat. No. 10,073,923). The editing of such sites may belimited so that the highly modular layout elements are preserved, e.g.,system 200 may prevent the user from moving components from one layoutelement to another. Furthermore, the layout elements may be associatedwith underlying content elements which are not part of the visual designand are thus not visually modified during site editing (e.g. the changesare typically limited to changes to content).

It will be appreciated that the situation may be different with freelyedited web sites, i.e., web sites created using the regular WBS editor30. The source and target templates may not correspond in the firstplace, and any existing similarities between the two templates may havebeen destroyed through changes made by the user. Thus, accurate mappingmay be impossible.

Template replacer 409 may provide a machine learning based solution tothis problem. Template replacer 409 may provide a partial solution,mapping some of the content, layout, and other changes made in thesource template while omitting some content which may be impossible tomap to the target template.

Template replacer 409 may also make use of hints added to the templatesby their creators (e.g., the WBS vendor 61 staff) which may conveyadditional information on template element equivalences. For example,WBS vendor 61 may mark different sections of different templates withsemantic markers (e.g., marking a given set of components with an “ourcompany history” marker). These markers may be comparable to the set ofcontent elements/layout elements defined in U.S. Pat. No. 10,073,923 ora may be a different set of group types which may be detected bytemplate replacer 409 for the template replacement. Other modules insite function updater 400, such as component grouper 401, componentgrouper labeler 402 and component orderer 404, may assist templatereplacer 409 to determine the group types. The resulting templatereplacement may also be similar to (and may share elements with) thetransformations performed by object transformer 405 and WBS importer407.

Template replacer 409 may include a specialized editor which may providean additional indication as to which content/layout was successfullytransferred (and how), and what was not transferred, and may also allowthe user to correct and complete the transfer results. This editor couldalso be integrated into WBS editor 30 (e.g., as a task list, a wizard ora workflow system).

It will be appreciated that a template replacement AI unit 310 used bytemplate replacer 409 may be trained with pairs of sites convertedbetween templates. The template replacer AI unit 310 may also be trainedwith follow up corrections made to the converted site using thespecialized editor described above, as well as changes made to the siteusing WBS editor 30. The template replacer AI unit 310 may also betrained with sets of templates for which content-equivalence hints havebeen specified (as described above) even if no or partial specificcontent has been inserted.

It will be further appreciated that training template replacement AIunit 310 may be difficult, as there may be very few relevant conversionsessions per template pair. As the number of templates grow, the numberof possible template pairs (source site template and target sitetemplate) may also grow. Template replacer 409 may generalize and maydetermine what the required operations are, given good examples.Template replacer 409 may employ, for example, the following approachesor a combination thereof. Template replacer 409 may train a separate peractivity AI unit 310 for a given template pair with all the conversionsessions for the given pair or may train a separate model for each groupof “similar” pairs (as defined by WBS vendor 61), or may train a singlemodel for all pairs together.

The collected training data may still include features identifying thesource and target templates and their parameters and attributes (e.g.,business domain and general style, as well as more detailed features).Training a model for each group of similar pairs or for all pairstogether (with the relevant pair information features) may be preferablesince in many cases there may be similar pairs, even in multipledomains.

For example, WBS vendor 61 may create a set A of elegant style templates(e.g., for fitness studios and clothing stores) with some similaritybetween all templates in A. Later WBS vendor 61 may similarly create aset B of formal style templates (for the same or different businessdomains). The user may then convert some sites created with templatesfrom group A to use with templates from group B. Such conversions maytypically occur within the same business domain (i.e. converting afitness studio A template to a fitness studio B template). In thisscenario, the template conversion AI unit 310 may benefit from beingtrained with information gathered from multiple conversion sessionsbetween group A templates and group B templates, typically with addedfeatures such as “group name” (A, B or others) and “business domain”.This could be much better than training a separate model for eachspecific pair [A member, B member] which was actually used.

Template replacer 409 may also use multiple per activity AI units 310for separate functionality areas (e.g., one to analyze the site'shierarchy and structure and one to analyze layout and additionalattributes) as described above for WBS importer 407.

As described herein above, sites created using site generation system 40may be highly structured. One major advantage of this highly structuredlayout element hierarchy is that sites may be easily re-generated aftertheir underlying data has been modified. System 200 may update therelevant content elements, re-evaluate content element-layout elementassociation and layout element arrangement and re-generate the layoutelement hierarchy and the resulting pages as required. On the otherhand, regular (manually edited) sites use a less rigid componenthierarchy. Their structure has evolved through a series of editingsessions, e.g., during the creation of an underlying template andediting of the actual web site. Such sites cannot simply be re-generatedwhen the underlying data has changed.

Thus, the user may desire to convert a manually edited site into theformat employed by site generation system 40. Manual site adapter 410may use one or more per activity AI units 310 to detect groups in theWBS component hierarchy and to determine semantic connections betweenthe components. Manual site adapter 410 may then match these groupsagainst the existing set of site generation system structures (includingcontent elements and visual site sections) and may construct thematching site generation system form of the site.

Similar to the functionality of WBS importer 407, manual site adaptionAI unit 310 may be trained on existing site pairs (pre- orpost-conversion sites). It may also be trained based on latermodifications made by the user to the post-conversion version (e.g.,through the site generation system limited editor).

Design suggester 411 may allow the user to select a set of components,query the WBS, receive alternative options for the selected setlayout/design, select a preferred layout/design option and apply thepreferred layout/design option to the selected component set. Designsuggester 411 may integrate suggested alternative quality ratings anddiversifications similar to that discussed in U.S. Pat. Nos. 9,996,566and 9,747,258.

It will be appreciated that design suggester 411 may base its offers onlayouts gathered from multiple sources (other sites in the WBS,pre-built templates, non-WBS sites, etc.) or on constructed layoutscreated based on the components in the selected set. Furthermore, whenapplying the preferred layout/design to the selected component set, theymay not match precisely, and system 200 be required to extend or modifythe elements of the suggested or preferred layouts/designs in order tocreate a match.

It will further be appreciated that design suggester 411 may benefitfrom the technologies described herein above by including designsuggesting AI units 310 supporting the following functions and receivinguser feedback gathered from the user's interaction with the system. Thisprocess may be done in a number of phases.

Phase 1 is layout gathering. As discussed herein above, per activity AIunits 310 may support importing from multiple sources, includingunderstanding and analysis of the imported layouts (such as the MLmodels 317 associated with WBS importer 407 as described herein above).

Design suggester 411 may implement and train a specific layoutextraction AI unit 310 for the creation of suggested layouts. Such aspecific layout extraction AI unit 310 may provide better results than afull-scale site importation model, as layout extraction AI unit 310 maybe required to extract only abstract layout information (which is moreabstract or generalized) and not full component information or actualcomponent content. For example, when importing a layout containing avideo component, a full layout extraction AI unit 310 may identify theexact video used and its display attributes, whereas an designsuggesting AI unit 31 may only need the video component frame positionand size and which associated video controls are included (as the actualvideo clip will be replaced by that specified by the user).

The design suggester AI unit 310 may be trained with user feedbackcollected from follow-up editing. Design suggester 411 may classifyfollow up changes, as not all changes indicate an error in the originalextraction. For example, design suggester AI unit 310 may interpret agiven combination of HTML elements in an imported site as an imagegallery, and the user may later change this into a video gallery. Thischange could indicate an error in the original understanding of theexamined site, or a decision by the user to change the component type inthe suggested layout. Design suggester 411 may implement such a changein classification based, for example, on how many users made this change(e.g., if many users made the same change to this layout, it is morelikely that the original extraction was erroneous).

Phase 2 is the selection of layouts to suggest and interaction with thesuggested layouts. In this post-query phase, design suggester 411 mayselect, rank and present the design alternatives to the user. Designsuggester 411 may employ a selection and ranking AI unit 310 to supportthese functions.

Such a selection and ranking AI unit 310 may initially be trained usingexamples of selection and ranking based on rating and diversificationalgorithms such as the ones described in U.S. Pat. Nos. 9,747,258 and9,996,566. Selection and ranking AI unit 310 may be further trainedbased on input from the user. The features used may therefore includethe original user-selected set passed to the query. The features couldinclude the actual component data (including type, layout, content, andattributes), as well as a semantic signature describing the componentset (as described in U.S. Pat. Nos. 9,747,258 and 9,996,566). Thefeatures may further include additional parameters and features such asuser parameters and site parameters (as described herein above). Suchfeatures would allow the design suggesting AI unit 310 to adapt tospecific user and community tastes, e.g., a regional preference for amore colorful or more formal design.

Other input features may further include the actual selection made bythe user, including possibly additional alternatives reviewed by theuser but not selected and the level of use of the resulting page (e.g.,was it published and when?).

Phase 3 is the adaptation of the selected component set to a selectedresult layout. It will be appreciated that such adaptation may also beperformed using an adaptation AI unit 310 trained for this task. Theadaptation AI unit 310 may be initially trained based on a componentmatching and adaptation algorithm. Later, it may be trained based onuser feedback as collected implicitly from changes made by the user tothe adapted layout.

Thus, an alternate design may be implemented using not just pre-definedcomponent import, query, matching, ranking, and diversificationalgorithms, but also using one or more per activity AI units 310 tosupport the required functionality. Such per activity AI units 310 maybe further trained using feedback collected from the users duringfurther sessions.

Thus, machine learning/artificial intelligence models may beincorporated into standard WBS functionality and editing tasks. Thesemodels may be trained and continually updated using feedback based onboth implicit and explicit information.

Furthermore, the previous discussion focused on websites hosted by thewebsite building system provider (which implements system 200). However,system 200 may be implemented with additional types of websites andother non-web digital creations. These may include, for example, thefollowing (or any combination thereof): full websites and websitesections (e.g., a subset of the website's pages) or sections of one ormore website pages, websites designed for regular desktop computerviewing, mobile websites and tablet-oriented websites, websites createdby a website building system but hosted externally (i.e., not by thewebsite building system vendor), websites running locally on a localserver installed on the user's machine and websites which serve as a U1and are hosted within other systems (including embedded systems andappliances).

Other types of websites and other non-web digital creations may alsoinclude websites or other displayed visual compositions hosted withinlarger systems, including (for example) pages hosted within socialnetworks (such as Facebook), blogs, portals (including video and audioportals which support user-customizable page such as YouTube channels),etc. This may include other types of remotely accessible online presencewhich are not regarded as a website.

Other types of websites and other non-web digital creations may alsoinclude interactive (mobile or otherwise) applications, including hybridapplications (which combine locally-installed elements with remotelyretrieved elements) and non-interactive digital creations, such ase-mails, newsletters, and other digital documents.

Unless specifically stated otherwise, as apparent from the precedingdiscussions, it is appreciated that, throughout the specification,discussions utilizing terms such as “processing,” “computing,”“calculating,” “determining,” or the like, refer to the action and/orprocesses of a general purpose computer of any type, such as aclient/server system, mobile computing devices, smart appliances, cloudcomputing units or similar electronic computing devices that manipulateand/or transform data within the computing system's registers and/ormemories into other data within the computing system's memories,registers or other such information storage, transmission or displaydevices.

Embodiments of the present invention may include apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the desired purposes, or it may comprise a computingdevice or system typically having at least one processor and at leastone memory, selectively activated or reconfigured by a computer programstored in the computer. The resultant apparatus when instructed bysoftware may turn the general-purpose computer into inventive elementsas discussed herein. The instructions may define the inventive device inoperation with the computer platform for which it is desired. Such acomputer program may be stored in a computer readable storage medium,such as, but not limited to, any type of disk, including optical disks,magnetic-optical disks, read-only memories (ROMs), volatile andnon-volatile memories, random access memories (RAMs), electricallyprogrammable read-only memories (EPROMs), electrically erasable andprogrammable read only memories (EEPROMs), magnetic or optical cards,Flash memory, disk-on-key or any other type of media suitable forstoring electronic instructions and capable of being coupled to acomputer system bus. The computer readable storage medium may also beimplemented in cloud storage.

Some general-purpose computers may comprise at least one communicationelement to enable communication with a data network and/or a mobilecommunications network.

The processes and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct a more specializedapparatus to perform the desired method. The desired structure for avariety of these systems will appear from the description below. Inaddition, embodiments of the present invention are not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implement theteachings of the invention as described herein.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

1. A website building system (WBS), the system comprising: a databasestoring at least the websites of a plurality of users of said WBS, andcomponents of said websites; and a processor implementing a machinelearning feedback-based proposal module, the module comprising: aplurality of per activity AI units, each unit to support one or morespecific activity related to said WBS and to provide at least one systemsuggestion to said users related to its said one or more specificactivity, each said per activity AI unit comprising at least onedynamically updatable machine learning model suitable for the activitysupported by its said per activity AI unit; and a feedback system todynamically update said machine learning models from a plurality ofdifferent kinds of feedback from at least said users, said feedbacksystem to evaluate a response quality of said feedback and to analyzesaid feedback to determine which one of said at least one machinelearning models to update.
 2. The WBS according to claim 1 and whereinsaid feedback system comprises: an implicit feedback handler to analyzeat least editing histories of said users to determine what furtheractivity said users perform on their websites and/or within said WBS andto generate therefrom implicit feedback to train relevant said machinelearning models.
 3. The WBS according to claim 2 and wherein saidfeedback system also comprises: an explicit feedback handler to analyzeat least user responses to said at least one system suggestion todetermine how said users respond to said at least one system suggestionand to generate therefrom explicit feedback to train relevant saidmachine learning models.
 4. The WBS according to claim 1 and alsocomprising an editor operative with said proposal module and whereinsaid tasks comprise at least one single component task within saideditor.
 5. The WBS according to claim 4 and wherein said at least onesingle component task comprises at least one of: image resolutionimprovement, face detection, portrait segmentation, objectionsegmentation, image cropping, image enhancement, logo creation and sitetext generation.
 6. The WBS according to claim 1 wherein said taskscomprise at least one multi-component task improving site function. 7.The WBS according to claim 6 and wherein said at least onemulti-component task comprises at least one of: component grouping,component group labeling, component ordering, object analysis, objecttransformation, desktop to mobile transformation, importation ofwebsites, template replacement, support of responsive editors andalternate design suggestion.
 8. The WBS according to claim 1 and whereineach said per activity AI unit comprises: an interaction generator toprovide at least one suggestion related to each said activity to saidusers based on the output of said at least one machine learning model.9. The WBS according to claim 1 and wherein said at least one machinelearning model is a model suited to the task and selected from one ormore of the following types of models: supervised, unsupervised,prediction algorithms, classification algorithms, clustering algorithms,association algorithms, time-series forecasting algorithms, image toimage models, sequence to sequence models, and Generative models. 10.The WBS according to claim 1 and wherein said feedback system comprisesat least one of: a response evaluator to evaluate a response quality offeedback responses from said users; a user evaluator to evaluate a userquality in giving feedback; a vendor handler to analyze feedback atleast from vendor staff of said WBS; and a community handler to analyzefeedback at least from a community of users.
 11. The WBS according toclaim 1 said proposal module to update at least one of said machinelearning models at one of the following: periodically and based on useractivity.
 12. The WBS according to claim 2 said implicit feedbackhandler to receive information gathered from within said WBS, whereinsaid information comprises disposition of the component and at least oneof the following: business information, user information, and siteinformation.
 13. The WBS according to claim 8, said interactiongenerator to provide one of a plurality of interactions as a function ofparameters of said user, of said website of said editing history. 14.The WBS according to claim 4 and wherein said at least one singlecomponent task is image cropping and wherein its said at least onemachine learning model is an image cropping model to infer that areas ofan image covered by overlapping components are non-important areas ofsaid image.
 15. The WBS according to claim 1 and also comprising a sitegeneration system to receive indications of non-important areas of abackground image from at least one said per activity AI unit and toplace layout elements over said non-important areas.
 16. A method for awebsite building system (WBS), the method comprising: storing at leastthe websites of a plurality of users of said WBS, and components of saidwebsites; having a plurality of per activity AI units; each per activityAI unit using at least one dynamically updatable machine learning modelto support one or more specific activity related to said WBS; from theoutput of said using, generating at least one system suggestion to saidusers related to said one or more specific activity; and dynamicallyupdating said machine learning models from a plurality of differentkinds of feedback from at least said users for updating said at leastone machine learning model, said updating comprising evaluating aresponse quality of said feedback and analyzing said feedback todetermine which one of said at least one machine learning models toupdate.
 17. The method according to claim 16 and wherein saiddynamically updating comprises: analyzing at least editing histories ofsaid users to determine what further activity said users perform ontheir websites and/or within said WBS; and generating therefrom implicitfeedback to train relevant said machine learning models.
 18. The methodaccording to claim 17 and wherein said dynamically updating alsocomprises: analyzing at least user responses to said at least one systemsuggestion to determine how said users respond to said at least onesystem suggestion; and generating therefrom explicit feedback to trainrelevant said machine learning models.
 19. The method according to claim16 and wherein said tasks comprise at least one single component taskwithin an editor.
 20. The method according to claim 19 and wherein saidat least one single component task comprises at least one of: imageresolution improvement, face detection, portrait segmentation, objectionsegmentation, image cropping, image enhancement, logo creation and sitetext generation.
 21. The method according to claim 16 wherein said taskscomprise at least one multi-component task improving site function. 22.The method according to claim 21 and wherein said at least onemulti-component task comprises at least one of: component grouping,component group labeling, component ordering, object analysis, objecttransformation, desktop to mobile transformation, importation ofwebsites, template replacement, support of responsive editors andalternate design suggestion.
 23. The method according to claim 16 andwherein said generating comprises producing a user interface for said atleast one suggestion.
 24. The method according to claim 16 and whereinsaid at least one machine learning model is a model suited to the taskand selected from one or more of the following types of models:supervised, unsupervised, prediction algorithms, classificationalgorithms, clustering algorithms, association algorithms, time-seriesforecasting algorithms, image to image models, sequence to sequencemodels, and Generative models.
 25. The method according to claim 16 andwherein said dynamically updating comprises at least one of: evaluatinga response quality of feedback responses from said users; evaluating auser quality in giving feedback; analyzing feedback at least from vendorstaff of said WBS; and analyzing feedback at least from a community ofusers.
 26. The method according to claim 16 and also comprising updatingat least one of said machine learning models at one of the following:periodically and based on user activity.
 27. The method according toclaim 17 and wherein said dynamically updating comprises receivinginformation gathered from within said WBS, wherein said informationcomprises disposition of the component and at least one of thefollowing: business information, user information, and site information.28. The method according to claim 23, wherein said producing comprisesdetermining said user interface as a function of parameters of saiduser, of said website of said editing history.
 29. The method accordingto claim 19 and wherein said at least one single component task is imagecropping and also comprising inferring, using its said at least onemachine learning model, that areas of an image covered by overlappingcomponents are non-important areas of said image
 30. The methodaccording to claim 16 and also comprising receiving indications ofnon-important areas of a background image from at least one said peractivity AI unit and placing layout elements over said non-importantareas.